The Experts below are selected from a list of 80424 Experts worldwide ranked by ideXlab platform
Jinfu Wang - One of the best experts on this subject based on the ideXlab platform.
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an efficient numerical algorithm for a novel theoretical breakup Kernel Function of bubble droplet in a turbulent flow
Chemical Engineering Science, 2004Co-Authors: Tiefeng Wang, Jinfu WangAbstract:Abstract An efficient numerical algorithm was proposed for “A novel theoretical breakup Kernel Function of bubble/droplet in a turbulent flow”. In this algorithm, a recursive method was used to calculate the involved triple integral, which reduced the calculation complexity of a triple integral to a double integral.
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a novel theoretical breakup Kernel Function for bubbles droplets in a turbulent flow
Chemical Engineering Science, 2003Co-Authors: Tiefeng Wang, Jinfu Wang, Yong JinAbstract:Several models for the daughter bubble/droplet size distribution are reviewed and a detailed discussion is given to get a better understanding of the daughter size distribution. A novel theoretical breakup Kernel Function for bubbles/droplets in a turbulent flow, based on an eddy-bubble/droplet collision method, is developed. It takes into account the energy distribution of turbulent eddies, effect of capillary pressure and surface energy increase during bubble/droplet breakup. An increase in the mother bubble/droplet size and energy dissipation rate increases the probability of unequal breakup. The model prediction is in good agreement with experimental results and the underlying physical situation.
Tiefeng Wang - One of the best experts on this subject based on the ideXlab platform.
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an efficient numerical algorithm for a novel theoretical breakup Kernel Function of bubble droplet in a turbulent flow
Chemical Engineering Science, 2004Co-Authors: Tiefeng Wang, Jinfu WangAbstract:Abstract An efficient numerical algorithm was proposed for “A novel theoretical breakup Kernel Function of bubble/droplet in a turbulent flow”. In this algorithm, a recursive method was used to calculate the involved triple integral, which reduced the calculation complexity of a triple integral to a double integral.
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a novel theoretical breakup Kernel Function for bubbles droplets in a turbulent flow
Chemical Engineering Science, 2003Co-Authors: Tiefeng Wang, Jinfu Wang, Yong JinAbstract:Several models for the daughter bubble/droplet size distribution are reviewed and a detailed discussion is given to get a better understanding of the daughter size distribution. A novel theoretical breakup Kernel Function for bubbles/droplets in a turbulent flow, based on an eddy-bubble/droplet collision method, is developed. It takes into account the energy distribution of turbulent eddies, effect of capillary pressure and surface energy increase during bubble/droplet breakup. An increase in the mother bubble/droplet size and energy dissipation rate increases the probability of unequal breakup. The model prediction is in good agreement with experimental results and the underlying physical situation.
Mohammad Noori - One of the best experts on this subject based on the ideXlab platform.
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a machine learning approach for structural damage detection using least square support vector machine based on a new combinational Kernel Function
Structural Health Monitoring-an International Journal, 2016Co-Authors: Ramin Ghiasi, P Torkzadeh, Mohammad NooriAbstract:Health assessment and monitoring of engineered systems have become one of the fastest growing multi-disciplinary research areas over the last two decades. One of the largest concerns in structural health monitoring is how to infer structural conditions from the measurements and the data collected by sensors. The ultimate aim is to detect the structural damages with a high level of certainty and hence to extend the life of structures. In this study, a new strategy for structural damage detection is proposed using least square support vector machines based on a new combinational Kernel. Thin plate spline Littlewood–Paley wavelet Kernel Function introduced in this article is a novel combinational Kernel Function, which combines thin plate spline radial basis Function Kernel with local characteristics and a modified Littlewood–Paley wavelet Kernel Function with global characteristics. During the process of structural damage detection, a social harmony search algorithm optimizes the parameters of least square ...
In-bok Lee - One of the best experts on this subject based on the ideXlab platform.
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Optimizing weighted Kernel Function for support vector machine by genetic algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Ha-nam Nguyen, Syng-yup Ohn, Soo-hoan Chae, Dong Ho Song, In-bok LeeAbstract:The problem of determining optimal decision model is a difficult combinatorial task in the fields of pattern classification, machine learning, and especially bioinformatics. Recently, support vector machine (SVM) has shown a better performance than conventional learning methods in many applications. This paper proposes a weighted Kernel Function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the weighted Kernel Function as the weighted sum of a set of different types of basis Kernel Functions such as neural, radial, and polynomial Kernels, which are trained by a learning method based on genetic algorithm. The weights of basis Kernel Functions in proposed Kernel are determined in learning phase and used as the parameters in the decision model in classification phase. The experiments on several clinical datasets such as colon cancer, leukemia cancer, and lung cancer datasets indicate that our weighted Kernel Function results in higher and more stable classification performance than other Kernel Functions. Our method also has comparable and sometimes better classification performance than other classification methods for certain applications.
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MICAI - Optimizing weighted Kernel Function for support vector machine by genetic algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Ha-nam Nguyen, Syng-yup Ohn, Soo-hoan Chae, Dong Ho Song, In-bok LeeAbstract:The problem of determining optimal decision model is a difficult combinatorial task in the fields of pattern classification, machine learning, and especially bioinformatics. Recently, support vector machine (SVM) has shown a better performance than conventional learning methods in many applications. This paper proposes a weighted Kernel Function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the weighted Kernel Function as the weighted sum of a set of different types of basis Kernel Functions such as neural, radial, and polynomial Kernels, which are trained by a learning method based on genetic algorithm. The weights of basis Kernel Functions in proposed Kernel are determined in learning phase and used as the parameters in the decision model in classification phase. The experiments on several clinical datasets such as colon cancer, leukemia cancer, and lung cancer datasets indicate that our weighted Kernel Function results in higher and more stable classification performance than other Kernel Functions. Our method also has comparable and sometimes better classification performance than other classification methods for certain applications.
Ha-nam Nguyen - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (1) - Unified Kernel Function and its training method for SVM
Neural Information Processing, 2006Co-Authors: Ha-nam Nguyen, Syng-yup OhnAbstract:This paper proposes a unified Kernel Function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the unified Kernel Function as the weighted sum of a set of different types of basis Kernel Functions such as neural, radial, and polynomial Kernels, which are trained by a new learning method based on genetic algorithm. The weights of basis Kernel Functions in the unified Kernel are determined in learning phase and used as the parameters in the decision model in the classification phase. The unified Kernel and the learning method were applied to obtain the optimal decision model for the classification of two public data sets for diagnosis of cancer diseases. The experiment showed fast convergence in learning phase and resulted in the optimal decision model with the better performance than other Kernels. Therefore, the proposed Kernel Function has the greater flexibility in representing a problem space than other Kernel Functions.
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Optimizing weighted Kernel Function for support vector machine by genetic algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Ha-nam Nguyen, Syng-yup Ohn, Soo-hoan Chae, Dong Ho Song, In-bok LeeAbstract:The problem of determining optimal decision model is a difficult combinatorial task in the fields of pattern classification, machine learning, and especially bioinformatics. Recently, support vector machine (SVM) has shown a better performance than conventional learning methods in many applications. This paper proposes a weighted Kernel Function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the weighted Kernel Function as the weighted sum of a set of different types of basis Kernel Functions such as neural, radial, and polynomial Kernels, which are trained by a learning method based on genetic algorithm. The weights of basis Kernel Functions in proposed Kernel are determined in learning phase and used as the parameters in the decision model in classification phase. The experiments on several clinical datasets such as colon cancer, leukemia cancer, and lung cancer datasets indicate that our weighted Kernel Function results in higher and more stable classification performance than other Kernel Functions. Our method also has comparable and sometimes better classification performance than other classification methods for certain applications.
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MICAI - Optimizing weighted Kernel Function for support vector machine by genetic algorithm
Lecture Notes in Computer Science, 2006Co-Authors: Ha-nam Nguyen, Syng-yup Ohn, Soo-hoan Chae, Dong Ho Song, In-bok LeeAbstract:The problem of determining optimal decision model is a difficult combinatorial task in the fields of pattern classification, machine learning, and especially bioinformatics. Recently, support vector machine (SVM) has shown a better performance than conventional learning methods in many applications. This paper proposes a weighted Kernel Function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the weighted Kernel Function as the weighted sum of a set of different types of basis Kernel Functions such as neural, radial, and polynomial Kernels, which are trained by a learning method based on genetic algorithm. The weights of basis Kernel Functions in proposed Kernel are determined in learning phase and used as the parameters in the decision model in classification phase. The experiments on several clinical datasets such as colon cancer, leukemia cancer, and lung cancer datasets indicate that our weighted Kernel Function results in higher and more stable classification performance than other Kernel Functions. Our method also has comparable and sometimes better classification performance than other classification methods for certain applications.
