Navegando por Autor "Takahashi, Ricardo Hiroshi Caldeira"
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Item Adjusting the cut-off and maximum pool size in RT-qPCR pool testing for SARS-CoV-2.(2021) Costa, Murilo Soares; Sato, Hugo I.; Rocha, Raissa Prado; Carvalho, Alex Fiorini Coelho; Guimarães, Nathalia Sernizon; Machado, Elaine Leandro; Alves, Claudia Regina Lindgren; Teixeira, Santuza Maria Ribeiro; Takahashi, Ricardo Hiroshi Caldeira; Tupinambás, Unaí; Fonseca, Flávio Guimarães daReverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) to detect SARS-CoV-2 RNA is an essential test to monitor the occurrence of COVID-19. A methodology is proposed for the determination of maximum pool size and adjustments of cut-off values of cycle threshold (Ct in RT-qPCR pool testing, to compensate for the dilution caused by pooling. The trade off between pool size and test sensitivity is stated explicitly. The procedure was designed to ensure that samples that would be detectable in individual testing remain detectable in pool testing. The proposed relaxation in cut-off is dependent on the pool size, allowing a relatively tight correction to avoid loss of detection of positive samples. The methodology was evaluated in a study of pool testing of adults attending a public emergency care unit, reference for COVID-19 in Belo Horizonte, Brazil, and presenting flu-like symptoms. Even samples on the edge of detectability in individual testing were detected correctly. The proposed procedure enhances the consistency of RT-qPCR pool testing by enforcing that the scales of detectability in pool processing and in individual sample processing are compatible. This may enhance the contribution of pool testing to large-scale testing for COVID-19.Item Control of flexible manufacturing systems under model uncertainty using supervisory control theory and evolutionary computation schedule synthesis.(2016) Pena, Patrícia Nascimento; Costa, Tatiana Alves; Ramalho, Regiane de Sousa e Silva; Takahashi, Ricardo Hiroshi CaldeiraA new approach for the problem of optimal task scheduling in flexible manufacturing systems is proposed in this work, as a combination of metaheuristic optimization techniques with the supervisory control theory of discrete-event systems. A specific encoding, the word-shuffling encoding, which avoids the generation of a large number of infeasible sequences, is employed. A metaheuristic method based on a Variable Neighborhood Search is then built using such an encoding. The optimization algorithm performs the search for the optimal schedules, while the supervisory control has the role of codifying all the problem constraints, allowing an efficient feasibility correction procedure, and avoiding schedules that are sensitive to uncertainties in the execution times associated with the plant operation. In this way, the proposed methodology achieves a system performance which is typical from model-predictive scheduling, combined with the robustness which is required from a structural control.Item Data-driven inference for the spatial scan statistic.(2011) Almeida, Alexandre Celestino Leite de; Duarte, Anderson Ribeiro; Duczmal, Luiz Henrique; Oliveira, Fernando Luiz Pereira de; Takahashi, Ricardo Hiroshi CaldeiraBackground: Kulldorff’s spatial scan statistic for aggregated area map s searches for cluster s of case s without specifying their size (numb er of areas) or geo graphic location in advance . Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not don e in an even manner for all possible cluster sizes .Results: A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypo thesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found un der null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. Conclusions : A practical procedure is provide d to make more accurate inferences about the most likely cluster found by the spatial scan statistic.Item Feedback-control operators for improved Pareto-set description : application to a polymer extrusion process.(2015) Carrano, Eduardo Gontijo; Coelho, Dayanne Gouveia; Cunha, António Gaspar; Wanner, Elizabeth Fialho; Takahashi, Ricardo Hiroshi CaldeiraThis paper presents a new class of operators for multiobjective evolutionary algorithms that are inspired on feedback-control techniques. The proposed operators, the archive-set reduction and the surface-filling crossover, have the purpose of enhancing the quality of the description of the Pareto-set in multiobjective optimization problems. They act on the Pareto -estimate sample set, performing operations that eliminate archive points in the most crowded regions, and generate new points in the less populated regions, leading to a dynamic equilibrium that tends to generate a uniform sampling of the efficient solution set. The internal parameters of those operators are coordinated by feedback-control inspired techniques, which ensure that the desired equilibrium is attained. Numerical experiments in some benchmark problems and in a real problem of optimization of a single screw extrusion system for polymer processing show that the proposed methodology is able to generate more detailed descriptions of Pareto-optimal fronts than the ones produced by usual algorithms.Item Hybrid multicriteria algorithms applied to structural design of wireless local area networks.(2018) Lima, Marlon Paolo; Takahashi, Ricardo Hiroshi Caldeira; Vieira, Marcos Augusto Menezes; Carrano, Eduardo GontijoThis manuscript presents a novel approach based on hybrid optimization techniques for planning Wireless Local Area Networks in two stages: i) network structure design for access point (AP) placement and channel assignment and ii) channel assignment enhancement. We consider two objective functions: network load balance and signal-to-interference-plus-noise ratio; and three hard constraints: maximum AP capacities, client demand attendance, and minimum coverage levels. The proposed algorithm delivers an approximation of the efficient solution set, considering the two functions described above. The results from two scenarios were compared to the following four approaches: two multiobjective evolutionary algorithms, a well-known commercial tool, and a greedy technique. Finally, the solutions were subjected to sensitivity analysis to validate their robustness regarding user mobility and AP failures.Item Multi-objective decision in machine learning.(2016) Medeiros, Talles Henrique de; Rocha, Honovan Paz; Torres, Frank Sill; Takahashi, Ricardo Hiroshi Caldeira; Braga, Antônio de PáduaThiswork presents a novel approach for decisionmaking for multi-objective binary classification problems. The purpose of the decision process is to select within a set of Pareto-optimal solutions, one model that minimizes the structural risk (generalization error). This new approach utilizes a kind of prior knowledge that, if available, allows the selection of a model that better represents the problem in question. Prior knowledge about the imprecisions of the collected data enables the identification of the region of equivalent solutions within the set of Pareto-optimal solutions. Results for binary classification problems with sets of synthetic and real data indicate equal or better performance in terms of decision efficiency compared to similar approaches.Item Multi-objective dynamic programming for spatial cluster detection.(2015) Moreira, Gladston Juliano Prates; Paquete, Luís; Duczmal, Luiz Henrique; Menotti, David; Takahashi, Ricardo Hiroshi CaldeiraThe detection and inference of arbitrarily shaped spatial clusters in aggregated geographical areas is described here as a multi-objective combinatorial optimization problem. A multi-objective dynamic programming algorithm, the Geo Dynamic Scan, is proposed for this formulation, finding a collection of Pareto-optimal solutions. It takes into account the geographical proximity between areas, thus allowing a disconnected subset of aggregated areas to be included in the efficient solutions set. It is shown that the collection of efficient solutions generated by this approach contains all the solutions maximizing the spatial scan statistic. The plurality of the efficient solutions set is potentially useful to analyze variations of the most likely cluster and to investigate covariates. Numerical simulations are conducted to evaluate the algorithm. A study case with Chagas’ disease clusters in Brazil is presented, with covariate analysis showing strong correlation of disease occurrence with environmental data.Item Multiobjective planning of indoor Wireless Local Area Networks using subpermutation-based hybrid algorithms.(2023) Lima, Marlon Paolo; Takahashi, Ricardo Hiroshi Caldeira; Vieira, Marcos Augusto Menezes; Carrano, Eduardo GontijoWireless Local Area Network (WLAN) has become the most popular technology for mobile Internet access in recent decades. This manuscript presents a novel approach, based on hybrid optimization algorithms, for planning WLANs. Two objective functions are optimized: to maximize network load balance and signal-to-noise ratio. In addition, constraints related to coverage, customer, and equipment demand are considered. A key aspect of the proposed algorithm is its new representation/decoding scheme, based on subpermutations, which considerably reduces the search space dimension. This structure guarantees feasibility of the obtained solutions and increases the computational efficiency of the method. Several tests were performed in two scenarios, one of them using real data from a large-scale WLAN. When compared to other three approaches, such results show that the proposed method provides solutions that reduce costs and improve the WLAN throughput.Item On a vector space representation in genetic algorithms for sensor scheduling in wireless sensor networks.(2014) Martins, Flávio Vinícius Cruzeiro; Carrano, Eduardo Gontijo; Wanner, Elizabeth Fialho; Takahashi, Ricardo Hiroshi Caldeira; Mateus, Geraldo Robson; Nakamura, Fabiola GuerraRecent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providingmeaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system’s dynamics. To the authors’ knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.Item On the performance degradation of dominance-based evolutionary algorithms in many-objective optimization.(2016) Santos, Thiago; Takahashi, Ricardo Hiroshi CaldeiraAbstract—In the last decade, it has become apparent that the performance of Pareto-dominance based evolutionary multiobjective optimization algorithms degrades as the number of objective functions of the problem, given by n, grows. This performance degradation has been the subject of several studies in the last years, but the exact mechanism behind this phenomenon has not been fully understood yet. This paper presents an analytical study of this phenomenon under problems with continuous variables, by a simple setup of quadratic objective functions with spherical contour curves and a symmetrical arrangement of the function minima location. Within such a setup, some analytical formulae are derived to describe the probability of the optimization progress as a function of the distance to the exact Pareto-set. A main conclusion is stated about the nature and structure of the performance degradation phenomenon in manyobjective problems: when a current solution reaches a that is an order of magnitude smaller than the length of the Pareto-set, the probability of finding a new point that dominates the current one is given by a power law function of with exponent (n−1). The dimension of the space of decision variables has no influence on that exponent. Those results give support to a discussion about some general directions that are currently under consideration within the research community.Item On the performance degradation of dominance-based evolutionary algorithms in many-objective optimization.(2018) Santos, Thiago Fontes; Takahashi, Ricardo Hiroshi CaldeiraIn the last decade, it has become apparent that the performance of Pareto-dominance based evolutionary multiobjective optimization algorithms degrades as the number of objective functions of the problem, given by n, grows. This performance degradation has been the subject of several studies in the last years, but the exact mechanism behind this phenomenon has not been fully understood yet. This paper presents an analytical study of this phenomenon under problems with continuous variables, by a simple setup of quadratic objective functions with spherical contour curves and a symmetrical arrangement of the function minima location. Within such a setup, some analytical formulae are derived to describe the probability of the optimization progress as a function of the distance λ to the exact Pareto-set. A main conclusion is stated about the nature and structure of the performance degradation phenomenon in manyobjective problems: when a current solution reaches a λ that is an order of magnitude smaller than the length of the Pareto-set, the probability of finding a new point that dominates the current one is given by a power law function of λ with exponent (n−1). The dimension of the space of decision variables has no influence on that exponent. Those results give support to a discussion about some general directions that are currently under consideration within the research community.Item SCO-Concat : a solution to a planning problem in flexible manufacturing systems using supervisory control theory and optimization techniques.(2018) Costa, Tatiana Alves; Pena, Patrícia Nascimento; Takahashi, Ricardo Hiroshi CaldeiraThis work presents a modified version of the SCO (Supervisory Control and Optimization) methodology, proposed in Pena et al. (Inf Sci 329:491–502, 2016) to deal with planning problems in flexible manufacturing systems. Although having proved to be an alternative to deal with this class of problems, the SCO methodology is limited by the fact that it can only be applied to deal with small batches of products. Previous works show that when considering manufacturing systems of a moderate degree of complexity, this approach is only efficient to generate solutions for batches containing very few products, as for larger batches, the necessary computational time to process a solution is very high. It is obvious that, for the problems in the real world, this dimension of production is very small, which, at first, makes the application of SCO methodology quite limited. Therefore, this work proposes a complementary approach to SCO, here called SCO-Concat, developed to carry out the planning in larger batches of production. The proposed methodology was tested in a plant of moderate size, and the results obtained show that planning for batches as large as desired can be achieved in an efficient manner by SCO-Concat at a very reduced computational cost.Item Vertical social distancing policy is ineffective to contain the COVID-19 pandemic.(2020) Duczmal, Luiz Henrique; Almeida, Alexandre Celestino Leite de; Duczmal, Denise Bulgarelli; Alves, Claudia Regina Lindgren; Magalhães, Flávia Costa Oliveira; Lima, Max Sousa de; Silva, Ivair Ramos; Takahashi, Ricardo Hiroshi CaldeiraConsidering numerical simulations, this study shows that the so-called vertical social distancing health policy is ineffective to contain the COVID-19 pandemic. We present the SEIR-Net model, for a network of social group interactions, as a development of the classic mathematical model of SEIR epidemics (Susceptible-Exposed-Infected (symptomatic and asymptomatic)- Removed). In the SEIR-Net model, we can simulate social contacts between groups divided by age groups and analyze different strategies of social distancing. In the vertical distancing policy, only older people are distanced, whereas in the horizontal distancing policy all age groups adhere to social distancing. These two scenarios are compared to a control scenario in which no intervention is made to distance people. The vertical distancing scenario is almost as bad as the control, both in terms of people infected and in the acceleration of cases. On the other hand, horizontal distancing, if applied with the same intensity in all age groups, significantly reduces the total infected people “flattening the disease growth curve”. Our analysis considers the city of Belo Horizonte, Minas Gerais State, Brazil, but similar conclusions apply to other cities as well. Code implementation of the model in R-language is provided in the supplementary material.Item Voronoi distance based prospective space-time scans for point data sets : a dengue fever cluster analysis in a southeast Brazilian town.(2011) Duczmal, Luiz Henrique; Moreira, Gladston Juliano Prates; Duczmal, Denise Bulgarelli; Takahashi, Ricardo Hiroshi Caldeira; Magalhães, Flávia Costa Oliveira; Bodevan, Emerson CottaThe Prospective Space-Time scan statistic (PST) is widely used for the evaluation of space-time clusters of point event data. Usually a window of cylindrical shape is employed, with a circular or elliptical base in the space domain. Recently, the concept of Minimum Spanning Tree (MST) was applied to specify the set of potential clusters, through the Density-Equalizing Euclidean MST (DEEMST) method, for the detection of arbitrarily shaped clusters. The original map is cartogram transformed, such that the control points are spread uniformly. That method is quite effective, but the cartogram construction is computationally expensive and complicated.