Michel Bessani

Determination of switching sequence of Service Restoration in Distribution Systems: Application and analysis on a real and large-scale radial system

It is computationally hard to solve the Service Restoration (SR) problem for large-scale Distribution Systems (DSs) without any system simplification, since this problem is combinatorial and non-linear, involving several constraints and objectives. The methodology named MEAN-MH+ES has proved able to generate feasible solutions (radial configuration attending all the operational constraints) with relatively soft computing and without requiring any network simplification in several tests performed on the real and large-scale DS of Londrina city (Brazil). The MEAN-MH+ES combines Multi-objective Evolutionary Algorithm with Node-Depth Encoding, Multiple-criteria tables, alarming Heuristic and an Exhaustive search. However, as the majority of the methodologies for solving the SR problem, the MEAN-MH+ES does not provide a feasible sequence of switching operations to reach the final configuration (the feasible solution) from the initial configuration (the configuration with the faulted areas identified and isolated). This paper proposes to incorporate a heuristic procedure into MEAN-MH+ES, which enable to provide a Feasible Sequence of Switching Operations (FSSO), that is, a switching operation sequence that generates only intermediate configurations that respect the operational constraints. The proposed heuristic procedure is confirmed on tests performed on the real and large-scale DS of Londrina city.

Evaluation of a Dental Caries Clinical Decision Support System.

Decision Support Systems (DSSs) aims to support professionals decision process. A specific area of application is the Clinical one, resulting in Clinical Decision Support Systems (CDSSs), focusing on Clinical Decision problems, like oncology, geriatrics, and dentistry. DSSs integrate expert knowledge through patternbased approaches. Bayesian Networks are probabilistic graph models that allow representation and inference on complex scenarios. BNs are used in different decision-making fields, e.g., Clinical Decision Support Systems. Traditionally, such models are learned using established databases. However, in situations where such data set is unavailable, the BN can be manually constructed converting expert knowledge in conditional probabilities. In this paper, we evaluate a Dental Caries Clinical Decision Support System which uses a BN to provide suggestions and represent clinical patterns. The evaluation methodology uses forward sampling to generated data from the BN. The generated data are separated into three groups, and each one is analyzed. The results show the certainty of the Bayesian Network for some scenarios. The analysis of the CDSS BN indicates that the system efficiently infers according to the pattern presented in the literature.

Dependence between cognitive impairment and metabolic syndrome applied to a Brazilian elderly dataset

Globally, the proportion of elderly individuals in the population has increased substantially in the last few decades. However, the risk factors that should be managed in advance to ensure a natural process of mental decline due to aging remain unknown. In this study, a dataset consisting of a Brazilian elderly sample was modelled using a Bayesian Network (BN) approach to uncover connections between cognitive performance measures and potential influence factors. Regarding its structure (a Directed Acyclic Graph), it was investigated the probabilistic dependence mechanism between two variables of medical interest: the suspected risk factor known as Metabolic Syndrome (MetS) and the indicator of mental decline referred to as Cognitive Impairment (CI). In this investigation, the concept known in the context of a BN as D-separation has been employed. Results of the conducted study revealed that the dependence between MetS and Cognitive Variables (CI and its direct determinants) in fact exists and depends on both Body Mass Index (BMI) and age.

Modeling issues on load flow calculation for meshed distribution systems

Load flow solution is required for determining a steady-state condition of electric power systems for various load demands. This motivated the development of several methods for load flow calculation in transmission and distribution systems. However, there are some scenarios of meshed operation in distribution systems that pose additional challenges for the direct application of load flow calculation methods. This paper reports a detailed study of these scenarios, some without a detailed analysis in the literature, highlighting the modeling issues of each one to perform a load flow calculation in meshed distribution systems. The paper also proposes some alternatives to overcome these modeling issues, which are related to the substation model and the reference bus of the network. To illustrate the main theoretical conclusions, computational simulations based on real distribution feeders from a Brazilian utility are presented and analyzed.

Identification of Directed Interactions in Kinematic Data during Running

The knowledge of motion dynamics during running activity is crucial to enhance the development of rehabilitation techniques and injury prevention programs. Recent studies investigated the interaction between joints, using several analysis techniques, as cross-correlation, sensitivity analysis, among others. However, the direction of the joints pairing is still not understood. This paper proposes a study of the influence direction pattern in healthy runners by using kinematic data together with partial directed coherence, a frequency approach of Granger causality. The analysis was divided into three anatomical planes, sagittal, frontal, and transverse, and using data from ankle, knee, hip, and trunk segments. Results indicate a predominance of proximal to distal influence during running, reflecting a centralized anatomic source of movements. These findings highlight the necessity of managing proximal joints movements, in addition to motor control and core (trunk and hip) strengthening training to lumbar spine, knee, and ankle injuries prevention and rehabilitation.

Modeling of an Insect Proprioceptor System based on Different Neuron Response Times

This paper analyzes neuronal spiking signals from the Desert Locust Femorotibial Chordotonal Organ (FeCO). The data comes from records of the insect neuronal response due to external stimulation. We measured the Inter-Spike Interval (ISI) and calculated Transfer Entropy for investigate different FeCO responses. ISI is a technique that measures the time between two spikes; and transfer entropy is a theoretical information measure used to find dependencies and causal relationships. We also use survival functions to assemble FeCO models. Furthermore, this work uses and compares results of two approaches, one with transfer entropy and other with ISI measures. The results indicate evidence to support the existence of more than one type of FeCO neuron.

Unsupervised clustering analyses of features extraction for a caries computer-assisted diagnosis using dental fluorescence images

Computer-assisted diagnoses (CAD) are performed by systems with embedded knowledge. These systems work as a second opinion to the physician and use patient data to infer diagnoses for health problems. Caries is the most common oral disease and directly affects both individuals and the society. Here we propose the use of dental fluorescence images as input of a caries computer-assisted diagnosis. We use texture descriptors together with statistical pattern recognition techniques to measure the descriptors performance for the caries classification task. The data set consists of 64 fluorescence images of in vitro healthy and carious teeth including different surfaces and lesions already diagnosed by an expert. The texture feature extraction was performed on fluorescence images using RGB and YCbCr color spaces, which generated 35 different descriptors for each sample. Principal components analysis was performed for the data interpretation and dimensionality reduction. Finally, unsupervised clustering was employed for the analysis of the relation between the output labeling and the diagnosis of the expert. The PCA result showed a high correlation between the extracted features; seven components were sufficient to represent 91.9% of the original feature vectors information. The unsupervised clustering output was compared with the expert classification resulting in an accuracy of 96.88%. The results show the high accuracy of the proposed approach in identifying carious and non-carious teeth. Therefore, the development of a CAD system for caries using such an approach appears to be promising.