The Experts below are selected from a list of 231201 Experts worldwide ranked by ideXlab platform
Kimito Funatsu - One of the best experts on this subject based on the ideXlab platform.
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inverse qspr qsar analysis for chemical structure generation from y to x
Journal of Chemical Information and Modeling, 2016Co-Authors: Tomoyuki Miyao, Hiromasa Kaneko, Kimito FunatsuAbstract:Retrieving descriptor information (x information) from a value of an Objective Variable (y) is a fundamental problem in inverse quantitative structure–property relationship (inverse-QSPR) analysis but challenging because of the complexity of the preimage function. Herewith, we propose using a cluster-wise multiple linear regression (cMLR) model as a QSPR model for inverse-QSPR analysis. x information is acquired as a probability density function by combining cMLR and the prior distribution modeled with a mixture of Gaussians (GMMs). Three case studies were conducted to demonstrate various aspects of the potential of cMLR. It was found that the predictive power of cMLR was superior to that of MLR, especially for data with nonlinearity. Moreover, it turned out that the applicability domain could be considered since the posterior distribution inherits the prior distribution’s feature (i.e., training data feature) and represents the possibility of having the desired property. Finally, a series of inverse anal...
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classification of the degradation of soft sensor models and discussion on adaptive models
Aiche Journal, 2013Co-Authors: Hiromasa Kaneko, Kimito FunatsuAbstract:Soft sensors are used widely to estimate a process Variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. It is called as the degradation of soft sensor models. In this study, we attempted to classify this degradation of models in terms of changes in an explanatory Variable and an Objective Variable, and the rapidity of the changes. Moreover, we discussed characteristics of adaptive soft sensor models, based on the classification results. By analyzing simulated data sets, we could obtain knowledge and information on appropriate adaptive models for each type of the degradation. Keyword: Process control, Soft sensor, Degradation, Adaptive model, Predictive ability
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a new process Variable and dynamics selection method based on a genetic algorithm based wavelength selection method
Aiche Journal, 2012Co-Authors: Hiromasa Kaneko, Kimito FunatsuAbstract:Soft sensors have been used in industrial plants to estimate process Variables that are difficult to measure online. Soft sensor models predicting an Objective Variable should be constructed with only important explanatory Variables in terms of predictive ability, better interpretation of models and lower measurement costs. Besides, some process Variables can affect an Objective Variable with time-delays. Therefore, we have proposed the methods for selecting important process Variables and optimal time-delays of each Variable simultaneously, by modifying the genetic algorithm-based wavelength selection method that is one of the wavelength selection methods in spectrum analysis. The proposed methods can select time-regions of process Variables as a unit by using process data that includes process Variables that are delayed in the range from zero to a set/given maximum value. The case study with simulation data and real industrial data confirmed that predictive, easy-to-interpret, and appropriate models were constructed using the proposed methods. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1829–1840, 2012
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development of high predictive soft sensor method and the application to industrial polymer processes
Asia-Pacific Journal of Chemical Engineering, 2012Co-Authors: Hiromasa Kaneko, Kimito FunatsuAbstract:In industrial plants, soft sensors are widely used to estimate process Variables that are difficult to measure online. One of the problems of soft sensors is that their predictive accuracy gradually decreases with changes in the state of the plants. Although regression models are reconstructed with new data to solve this problem, some problems remain in practice. Hence, it is attempted to construct soft sensor models based upon the time difference of an Objective Variable and that of explanatory Variables for reducing the effects of deterioration with age such as the drift and gradual changes in the state of plants. However, the time difference model cannot account for the nonlinearity in process Variables. Therefore, to consider the nonlinearity and the effects of changes with age, we have proposed to construct time difference models after modeling nonlinear relationship between and among process Variables. Variables obtained by physical models or those calculated by statistical nonlinear regression methods are used to consider the nonlinearity, and then, a time difference model is constructed including these Variables. First, we verified the superiority of the proposed methods over traditional ones. Then, we applied these methods to the actual industrial data obtained during an industrial polymer process. The proposed models achieved high predictive accuracy for melt flow rate and density, and the bias of the prediction errors could be eased in both cases by using the proposed methods. We confirmed the usefulness of the proposed methods without reconstruction of soft sensor models. © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.
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a soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy
Chemometrics and Intelligent Laboratory Systems, 2011Co-Authors: Hiromasa Kaneko, Kimito FunatsuAbstract:Abstract Soft sensors are widely used to estimate process Variables that are difficult to measure online. However, their predictive accuracy gradually decreases with changes in the state of the plants. We have been constructing soft sensor models based on the time difference of an Objective Variable, y , and that of explanatory Variables (time difference models) for reducing the effects of deterioration with age such as the drift without model reconstruction. In this paper, we have attempted to improve and estimate the prediction accuracy of time difference models, and proposed to handle multiple y -values predicted from multiple intervals of time difference. A weighted average is a final predicted value and the standard deviation is an index of its prediction accuracy. This method was applied to real industrial data and then, could predict more number of data with higher predictive accuracy and estimate the prediction errors more accurately than traditional ones.
Hiromasa Kaneko - One of the best experts on this subject based on the ideXlab platform.
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inverse qspr qsar analysis for chemical structure generation from y to x
Journal of Chemical Information and Modeling, 2016Co-Authors: Tomoyuki Miyao, Hiromasa Kaneko, Kimito FunatsuAbstract:Retrieving descriptor information (x information) from a value of an Objective Variable (y) is a fundamental problem in inverse quantitative structure–property relationship (inverse-QSPR) analysis but challenging because of the complexity of the preimage function. Herewith, we propose using a cluster-wise multiple linear regression (cMLR) model as a QSPR model for inverse-QSPR analysis. x information is acquired as a probability density function by combining cMLR and the prior distribution modeled with a mixture of Gaussians (GMMs). Three case studies were conducted to demonstrate various aspects of the potential of cMLR. It was found that the predictive power of cMLR was superior to that of MLR, especially for data with nonlinearity. Moreover, it turned out that the applicability domain could be considered since the posterior distribution inherits the prior distribution’s feature (i.e., training data feature) and represents the possibility of having the desired property. Finally, a series of inverse anal...
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classification of the degradation of soft sensor models and discussion on adaptive models
Aiche Journal, 2013Co-Authors: Hiromasa Kaneko, Kimito FunatsuAbstract:Soft sensors are used widely to estimate a process Variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants. It is called as the degradation of soft sensor models. In this study, we attempted to classify this degradation of models in terms of changes in an explanatory Variable and an Objective Variable, and the rapidity of the changes. Moreover, we discussed characteristics of adaptive soft sensor models, based on the classification results. By analyzing simulated data sets, we could obtain knowledge and information on appropriate adaptive models for each type of the degradation. Keyword: Process control, Soft sensor, Degradation, Adaptive model, Predictive ability
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a new process Variable and dynamics selection method based on a genetic algorithm based wavelength selection method
Aiche Journal, 2012Co-Authors: Hiromasa Kaneko, Kimito FunatsuAbstract:Soft sensors have been used in industrial plants to estimate process Variables that are difficult to measure online. Soft sensor models predicting an Objective Variable should be constructed with only important explanatory Variables in terms of predictive ability, better interpretation of models and lower measurement costs. Besides, some process Variables can affect an Objective Variable with time-delays. Therefore, we have proposed the methods for selecting important process Variables and optimal time-delays of each Variable simultaneously, by modifying the genetic algorithm-based wavelength selection method that is one of the wavelength selection methods in spectrum analysis. The proposed methods can select time-regions of process Variables as a unit by using process data that includes process Variables that are delayed in the range from zero to a set/given maximum value. The case study with simulation data and real industrial data confirmed that predictive, easy-to-interpret, and appropriate models were constructed using the proposed methods. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1829–1840, 2012
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development of high predictive soft sensor method and the application to industrial polymer processes
Asia-Pacific Journal of Chemical Engineering, 2012Co-Authors: Hiromasa Kaneko, Kimito FunatsuAbstract:In industrial plants, soft sensors are widely used to estimate process Variables that are difficult to measure online. One of the problems of soft sensors is that their predictive accuracy gradually decreases with changes in the state of the plants. Although regression models are reconstructed with new data to solve this problem, some problems remain in practice. Hence, it is attempted to construct soft sensor models based upon the time difference of an Objective Variable and that of explanatory Variables for reducing the effects of deterioration with age such as the drift and gradual changes in the state of plants. However, the time difference model cannot account for the nonlinearity in process Variables. Therefore, to consider the nonlinearity and the effects of changes with age, we have proposed to construct time difference models after modeling nonlinear relationship between and among process Variables. Variables obtained by physical models or those calculated by statistical nonlinear regression methods are used to consider the nonlinearity, and then, a time difference model is constructed including these Variables. First, we verified the superiority of the proposed methods over traditional ones. Then, we applied these methods to the actual industrial data obtained during an industrial polymer process. The proposed models achieved high predictive accuracy for melt flow rate and density, and the bias of the prediction errors could be eased in both cases by using the proposed methods. We confirmed the usefulness of the proposed methods without reconstruction of soft sensor models. © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.
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a soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy
Chemometrics and Intelligent Laboratory Systems, 2011Co-Authors: Hiromasa Kaneko, Kimito FunatsuAbstract:Abstract Soft sensors are widely used to estimate process Variables that are difficult to measure online. However, their predictive accuracy gradually decreases with changes in the state of the plants. We have been constructing soft sensor models based on the time difference of an Objective Variable, y , and that of explanatory Variables (time difference models) for reducing the effects of deterioration with age such as the drift without model reconstruction. In this paper, we have attempted to improve and estimate the prediction accuracy of time difference models, and proposed to handle multiple y -values predicted from multiple intervals of time difference. A weighted average is a final predicted value and the standard deviation is an index of its prediction accuracy. This method was applied to real industrial data and then, could predict more number of data with higher predictive accuracy and estimate the prediction errors more accurately than traditional ones.
J Ferruz - One of the best experts on this subject based on the ideXlab platform.
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applying the movns multi Objective Variable neighborhood search algorithm to solve the path planning problem in mobile robotics
Expert Systems With Applications, 2016Co-Authors: Alejandro Hidalgopaniagua, Miguel A Vegarodriguez, J FerruzAbstract:Nowadays, robots are playing a fundamental role.The Path Planning problem is one of the most researched topics in mobile robotics.A new multi-Objective approach based on Variable Neighborhood Search is proposed.Three Objectives were optimized: path safety, path length, and path smoothness.We used eight realistic scenarios and compared with the state of the art. Mobile robots must calculate the appropriate navigation path before starting to move to its destination. This calculation is known as the Path Planning (PP) problem. The PP problem is one of the most researched topics in mobile robotics. Taking into account that the PP problem is an NP-hard problem, Multi-Objective Evolutionary Algorithms (MOEAs) are good candidates to solve this problem. In this work, a new multi-Objective evolutionary approach based on the Variable Neighborhood Search (MOVNS) is proposed to solve the PP problem. To the best of our knowledge, this is the first time that MOVNS is proposed to solve the path planning of mobile robots. The proposed MOVNS handles three different Objectives in order to obtain accurate and efficient paths. These Objectives are: the path safety, the path length, and the path smoothness (related to the energy consumption). Furthermore, in order to test the proposed MOEA, we have used eight realistic scenarios for the paths calculation. On the other hand, we also compared our proposal with other approaches of the state of the art, showing the advantages of MOVNS. In particular, in order to evaluate the obtained results we applied different quality metrics. Moreover, to demonstrate the statistical robustness of the obtained results we also performed a statistical analysis. Finally, the study shows that the proposed MOVNS is a good alternative to solve the PP problem, producing good paths with less length, more safety, and more smooth movements. We think this is an important contribution to the mobile robotics, and therefore, to the field of expert and intelligent systems.
K. Devika - One of the best experts on this subject based on the ideXlab platform.
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Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food
International Journal of Production Economics, 2014Co-Authors: Kannan Govindan, Amin Jafarian, Roohollah Khodaverdi, K. DevikaAbstract:Increasing environmental, legislative, and social concerns are forcing companies to take a fresh view of the impact of supply chain operations on environment and society when designing a sustainable supply chain. A challenging task in today's food industry is distributing high quality perishable foods throughout the food supply chain. This paper proposes a multi-Objective optimization model by integrating sustainability in decision-making, on distribution in a perishable food supply chain network (SCN). It introduces a two-echelon location-routing problem with time-windows (2E-LRPTW) for sustainable SCN design and optimizing economical and environmental Objectives in a perishable food SCN. The goal of 2E-LRPTW is to determine the number and location facilities and to optimize the amount of products delivered to lower stages and routes at each level. It also aims to reduce costs caused by carbon footprint and greenhouse gas emissions throughout the network. The proposed method includes a novel multi-Objective hybrid approach called MHPV, a hybrid of two known multi-Objective algorithms: namely, multi-Objective particle swarm optimization (MOPSO) and adapted multi-Objective Variable neighborhood search (AMOVNS). MHPV features two strategies for leader selection procedures (LSP), (i.e. Grids) and crowding distance is compared to common genetic algorithms based on metaheuristics (i.e. MOGA, NRGA and NSGA-II). Results indicate that the hybrid approach achieves better solutions compared to others, and that crowding distance method for LSP outperforms the former Grids method. © 2013 Elsevier B.V.
Kannan Govindan - One of the best experts on this subject based on the ideXlab platform.
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bi Objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi Objective metaheuristic
Computers & Operations Research, 2015Co-Authors: Kannan Govindan, Ahmad Jafarian, Vahid NourbakhshAbstract:Sustainability has been considered as a growing concern in supply chain network design (SCND) and in the order allocation problem (OAP). Accordingly, there still exists a gap in the quantitative modeling of sustainable SCND that consists of OAP. In this article, we cover this gap through simultaneously considering the sustainable OAP in the sustainable SCND as a strategic decision. The proposed supply chain network is composed of five echelons including suppliers classified in different classes, plants, distribution centers that dispatch products via two different ways, direct shipment, and cross-docks, to satisfy stochastic demand received from a set of retailers. The problem has been mathematically formulated as a multi-Objective optimization model that aims at minimizing the total costs and environmental effect of integrating SCND and OAP, simultaneously. To tackle the addressed problem, a novel multi-Objective hybrid approach called MOHEV with two strategies for its best particle selection procedure (BPSP), minimum distance, and crowding distance is proposed. MOHEV is constructed through hybridization of two multi-Objective algorithms, namely the adapted multi-Objective electromagnetism mechanism algorithm (AMOEMA) and adapted multi-Objective Variable neighborhood search (AMOVNS). According to achieved results, MOHEV achieves better solutions compared with the others, and also crowding distance method for BPSP outperforms minimum distance. Finally, a case study for an automobile industry is used to demonstrate the applicability of the approach.
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Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food
International Journal of Production Economics, 2014Co-Authors: Kannan Govindan, Amin Jafarian, Roohollah Khodaverdi, K. DevikaAbstract:Increasing environmental, legislative, and social concerns are forcing companies to take a fresh view of the impact of supply chain operations on environment and society when designing a sustainable supply chain. A challenging task in today's food industry is distributing high quality perishable foods throughout the food supply chain. This paper proposes a multi-Objective optimization model by integrating sustainability in decision-making, on distribution in a perishable food supply chain network (SCN). It introduces a two-echelon location-routing problem with time-windows (2E-LRPTW) for sustainable SCN design and optimizing economical and environmental Objectives in a perishable food SCN. The goal of 2E-LRPTW is to determine the number and location facilities and to optimize the amount of products delivered to lower stages and routes at each level. It also aims to reduce costs caused by carbon footprint and greenhouse gas emissions throughout the network. The proposed method includes a novel multi-Objective hybrid approach called MHPV, a hybrid of two known multi-Objective algorithms: namely, multi-Objective particle swarm optimization (MOPSO) and adapted multi-Objective Variable neighborhood search (AMOVNS). MHPV features two strategies for leader selection procedures (LSP), (i.e. Grids) and crowding distance is compared to common genetic algorithms based on metaheuristics (i.e. MOGA, NRGA and NSGA-II). Results indicate that the hybrid approach achieves better solutions compared to others, and that crowding distance method for LSP outperforms the former Grids method. © 2013 Elsevier B.V.