• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.49
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• pp.115-123
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• 1999

An optimization problem is to select the best of many possible design alternatives in a complex design space. Genetic algorithms, one of the numerous techniques to search optimal solution, have been successfully applied to various problems (for example, parameter tuning in expert systems, structural systems with a mix of continuous, integer and discrete design variables) that could not have been readily solved with more conventional computational technique. But, conventional genetic algorithms are ill defined for two classes of problems, ie., penalty function and fitness scaling. Therefore, this paper develops Improved genetic algorithms(IGA) to solve these problems. As a case study, numerical examples are demonstrated to show the effectiveness of the Improved genetic algorithms.

• Journal of Integrative Natural Science
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• v.9 no.1
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• pp.41-61
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• 2016

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Phosphoinositide 3-kinases (PI3Ks) play important role in Non-Small Cell Lung Cancer. PI3Ks constitute a lipid kinase family which modulates the function of numerous substrates involved in the regulation of cell survival, cell cycle progression and cellular growth. Herein, we describe the ligand based pharmacophore combined with molecular docking studies methods to identify new potent PI3K inhibitors. Several pharmacophore models were generated and validated by Guner-Henry scoring Method. The best models were utilized as 3D pharmacophore query to screen against ZINC database (Chemical and Natural) and the retrieved hits were further validated by fitness score, Lipinski's rule of five. Finally four compounds were found to have good potential and they may act as novel lead compounds for PI3K inhibitor designing.

• Structural Engineering and Mechanics
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• v.25 no.6
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• pp.637-652
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• 2007

This paper presents stacking sequence optimization of laminated angle-ply cylindrical panel based on natural frequency. Finite element method (FEM) is used to obtain the vibration characteristic of an anisotropic panel using the first order shear deformation theory(FSDT) and genetic algorithm (GA) is used to obtain the optimal stacking sequence of the layers. Cylindrical panel has finite length and arbitrary boundary conditions. The thicknesses of the layers are assumed constant and their angles are specified as design variables. The effect of the number of plies and boundary conditions in the fitness function is considered. Numerical examples are presented for four, six and eight layered anisotropic cylindrical panels.

• ETRI Journal
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• v.43 no.2
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• pp.324-331
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• 2021

Space-based automatic dependent surveillance-broadcast (ADS-B) is an important emerging augmentation of existing ground-based ADS-B systems. In this paper, the problem of space-based ultra-long-range reception processing of ADS-B signals is described. We first introduce a header detection method for accurately determining the pulse position of a weak ADS-B signal. We designed a signal encoding method, shaping method, and fitness function. We then employed a genetic algorithm to perform high-precision frequency and phase estimations of the detected weak signal. The advantage of this algorithm is that it can simultaneously estimate the frequency and phase, meaning a direct coherent demodulation can be implemented. To address the computational complexity of the genetic algorithm, we improved the ratio algorithm for frequency estimation and raised the accuracy beyond that of the original ratio algorithm with only a slight increase in the computational complexity using relatively few sampling points.

• Ocean Systems Engineering
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• v.9 no.2
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• pp.179-190
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• 2019

This paper deals with the problem of the global stabilization for a class of ocean structure systems. It is well known that, in general, the global asymptotic stability of the ocean structure subsystems does not imply the global asymptotic stability of the composite closed-loop system. The classical fuzzy inference methods cannot work to their full potential in such circumstances because given knowledge does not cover the entire problem domain. However, requirements of fuzzy systems may change over time and therefore, the use of a static rule base may affect the effectiveness of fuzzy rule interpolation due to the absence of the most concurrent (dynamic) rules. Designing a dynamic rule base yet needs additional information. In this paper, we demonstrate this proposed methodology is a flexible and general approach, with no theoretical restriction over the employment of any particular interpolation in performing interpolation nor in the computational mechanisms to implement fitness evaluation and rule promotion.

• Transactions of Materials Processing
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• v.30 no.1
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• pp.27-34
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• 2021

In this study, response surface method (RSM) was used in modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goals of optimization were the maximum forming angle, minimum thickness reduction, and minimum surface roughness, with varying values in response to changes in production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model for modeling the variations in the forming angle, thickness reduction, and surface roughness in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process based on experimental design. The results showed that RSM can be effectively used to control the forming angle, thickness reduction, and surface roughness.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.223-228
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• 2021

As a consequence of sudden outbreak of COVID-19 pandemic worldwide, educational institutes around the globe are forced to switch from traditional learning systems to e-learning systems. This has led to a variety of technology-driven pedagogies in e-teaching as well as e-learning. In order to take the best advantage, an appropriate understanding of the cognitive capability is of prime importance. This paper presents an intelligent cognitive maturity recognition system for confidence-based e-learning. We gather the data from actual test environment by involving a number of students and academicians to act as experts. Then a Genetic Programming based simulation and modeling is applied to generate a generalized classifier in the form of a mathematical expression. The simulation is derived towards an optimal space by carefully designed fitness function and assigning a range to each of the class labels. Experimental results validate that the proposed method yields comparative and superior results which makes it feasible to be used in real world scenarios.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.139-144
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• 2022

Technological advancements taken the health care industry by a storm by embedding sensors in human body to measure their vitals. These smart solutions provide better and flexible health care to patients, and also easy monitoring for the medical practitioners. However, these innovative solutions provide their own set of challenges. The major challenge faced by embedding sensors in body is the issue of lack of infinite energy source. This work presents a meta-heuristic based routing model using modified PSO, and adopts an energy harvesting scheme to improve the network lifetime. The routing process is governed by modifying the fitness function of PSO to include charge, temperature and other vital factors required for node selection. A reactive routing model is adopted to ensure reliable packet delivery. Experiments have been performed and comparisons indicate that the proposed Energy Harvesting and Modified PSO (EHMP) model demonstrates low overhead, higher network lifetime and better network stability.

• BMB Reports
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• v.55 no.12
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• pp.577-586
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• 2022

Stress granules (SGs) are stress-induced subcellular compartments, which carry out a particular function to cope with stress. These granules protect cells from stress-related damage and cell death through dynamic sequestration of numerous ribonucleoproteins (RNPs) and signaling proteins, thereby promoting cell survival under both physiological and pathological condition. During tumorigenesis, cancer cells are repeatedly exposed to diverse stress stimuli from the tumor microenvironment, and the dynamics of SGs is often modulated due to the alteration of gene expression patterns in cancer cells, leading to tumor progression as well as resistance to anticancer treatment. In this mini review, we provide a brief discussion about our current understanding of the fundamental roles of SGs during physiological stress and the effect of dysregulated SGs on cancer cell fitness and cancer therapy.