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A cost-effective instrumented walkway for measuring ground reaction forces in rats to assess gait pattern
No full textAnimal experiments have gained importance in human diseases studies, namely neurological diseases and its biomechanical and physiological aspects. As a model of human disease, the rat offers many advantages over other organisms. For the biomechanical aspects of locomotion these studies are based on the analysis of animals' kinetic parameters, accessed through a locomotion measurement system. However, these systems are not yet thoroughly developed, are still scarce and are also very expensive when developed for studies using small rodents. In this paper, a system capable of measuring contact forces of small rodents is presented. The platform hardware is based on a 5 x 3 matrix of ultra-sensitive force sensors that produce a set of signals acquired in a LabVIEW (TM) environment, used for data acquisition and processing. The post processing steps include the removal of null data, curve normalization related to the rat's weight and expressed as percentage of passage, resulting in a gait pattern. The proposed cost-effective system has achieved excellent results regarding the locomotion profile of healthy animals
A new brain emotional learning Simulink ® toolbox for control systems design * *This work was funded by the ERDF – European Regional Development Fund through the COMPETE Programme and by Portuguese funds through the FCT – Fundação para a Ciência e a Tecnologia within the project POCI-01-0145-FEDER-006961.
No full textTowards understanding consumers' quality evaluation of online health information: A case study
Get PDFFrom Single to Many-objective PID Controller Design using Particle Swarm Optimization
No full textProportional, integrative and derivative (PID) controllers are among the most used in industrial control applications. Classical PID controller design methodologies can be significantly improved by incorporating recent computational intelligence techniques. Two techniques based on particle swarm optimization (PSO) algorithms are proposed to design PI-PID controllers. Both control design methodologies are directed to optimize PI-PID controller gains using two degrees-of-freedom control configurations, subjected to frequency domain robustness constraints. The first technique proposes a single-objective PSO algorithm, to sequentially design a two degrees-of-freedom control structure, considering the optimization of load disturbance rejection followed by set-point tracking optimization. The second technique proposes a many-objective PSO algorithm, to design a two degrees-of-freedom control structure, considering simultaneously, the optimization of four different design criteria. In the many-objective case, the control engineer may select the most adequate solution among the resulting optimal Pareto set. Simulation results are presented showing the effectiveness of the proposed PI-PID design techniques, in comparison with both classic and optimization based methods
Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey
No full textEpilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity
A global perspective on an emotional learning model proposal
Get PDFExisting literature argues that emotions have a significant impact on the majority of human activities and functions. The learning process is one of the activities on which emotions have a direct influence. Thus, understanding the manner in which emotions change the students' learning process is not only very important but it can also allow to improve the existing learning models. Currently, in the majority of situations, the teacher serves as a facilitator between the student and the learning course, and through a constant analysis of the student's behaviour, emotions and achievements, he constantly performs adjustments to the teaching process in order to meet the students' needs and goals. Thus far, in online learning environments there is no easy way for teachers to analyse students' behaviour and emotions. A possible solution to this problem can be the development of mechanisms that enable computers to automatically detect students' emotions and adapt the learning process in order to meet students' real needs. An emotional learning model was described and a software prototype was developed and tested, in order to find out whether it performs live identification of the students' emotions, by using affective computing techniques, and whether it automatically performs adjustments to their individual learning process. Through a deeper analysis and multi-disciplinary discussion of the achieved results it is possible to acknowledge that not only emotions impact students' learning, but also that an application that performs live emotion recognition and which integrates this feature with adjustable online learning environments will trigger improvements in students' learning
Exploratory data analysis for interval compositional data
Get PDFCompositional data are considered as data where relative contributions of parts on a whole, conveyed by (log-)ratios between them, are essential for the analysis. In Symbolic Data Analysis (SDA), we are in the framework of interval data when elements are characterized by variables whose values are intervals on (Formula presented.) representing inherent variability. In this paper, we address the special problem of the analysis of interval compositions, i.e., when the interval data are obtained by the aggregation of compositions. It is assumed that the interval information is represented by the respective midpoints and ranges, and both sources of information are considered as compositions. In this context, we introduce the representation of interval data as three-way data. In the framework of the log-ratio approach from compositional data analysis, it is outlined how interval compositions can be treated in an exploratory context. The goal of the analysis is to represent the compositions by coordinates which are interpretable in terms of the original compositional parts. This is achieved by summarizing all relative information (logratios) about each part into one coordinate from the coordinate system. Based on an example from the European Union Statistics on Income and Living Conditions (EU-SILC), several possibilities for an exploratory data analysis approach for interval compositions are outlined and investigated. © 2016 Springer-Verlag Berlin Heidelber
Don't go in there! using the APEX framework in the design of ambient assisted living systems
Get PDFAn approach to design Ambient Assisted Living systems is presented, which is based on APEX, a framework for prototyping ubiquitous environments. The approach is illustrated through the design of a smart environment within a care home for older people. Prototypes allow participants in the design process to experience the proposed design and enable developers to explore design alternatives rapidly. APEX provides the means to explore alternative environment designs virtually. The prototypes developed with APEX offered a mediating representation, allowing users to be involved in the design process. A group of residents in a city-based care home were involved in the design. The paper describes the design process as well as lessons learned for the future design of AAL systems. © 2017, Springer-Verlag Berlin Heidelberg
A multi-temporal optimal power flow for managing storage and demand flexibility in LV networks
No full textImpacts of Operational Variability and Uncertainty on Distributed Generation Investment Planning: A Comprehensive Sensitivity Analysis
No full textThis paper presents a comprehensive sensitivity analysis to identify the uncertain parameters which significantly influence the decision-making process in distributed generation (DG) investments and quantify their degree of influence. To perform the analysis, a DG investment planning model is formulated as a novel multistage and multiscenario optimization problem. Moreover, to ensure tractability and make use of exact solution methods, the entire problem is kept as a mixed-integer linear programming optimization. A real-world distribution network system is used to carry out the analysis. The results of the analysis generally show that uncertainty as well as operational variability of the considered parameters have meaningful impacts on investment decisions of DG. The degree of influence varies from one parameter to another. But, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. Hence, the analysismade in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices