• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2853-2873
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• 2013

Over the last few years, one of the most common purposes of smart homes is to provide human centric services in the domain of u-healthcare by analyzing inhabitants' daily living. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. Smart homes indicate variation in terms of performed activities, deployed sensors, environment settings, and inhabitants' characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. This observation has motivated towards combining multiple classifiers to take advantage of their complementary performance for high accuracy. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with Genetic Algorithm (GA). Our proposed method combines the measurement level output of different classifiers for each activity class to make up the ensemble. For the evaluation of the proposed method, experiments are performed on three real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models. The significant improvement is achieved from 0.82 to 0.90 in the F-measures of recognized activities as compare to existing methods.

• Journal of Microbiology and Biotechnology
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• v.1 no.1
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• pp.54-62
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• 1991

Cyclodextrin glucanotransferase(CGTase) derived from Bacillus macerans was immobilized by (1) covalent linkage on chitosan and chitin with glutaraldehyde, (2) adsorption on DEAE-cellulose and Amberite IRA 900 after succinylation, and (3) entrapment on alginate and polyacrylamide by cross linking. Adsorption on Amberite IRA 900 and covalent linking on chitosan were identified to be the most suitable immobilization methods considering the yield of activity and stability of immobilized CGTase. The enzymatic properties of immobilized CGTase were investigated and compared with those of the soluble CGTase. Thermal stability of CGTase immobilized on chitosan was increased from 50 to $55^{\circ}C$, and the optimum temperature of CGTase immobilized on Amberite IRA 900 was shifted from 55 to $50^{\circ}C$. The effect of molecular size of soluble starch (substrate) on immobilized CGTase investigated using partially liquefied substrates with different dextrose equivalent(DE). Cyclodextrin(CD) conversion yield augmented according to the increase of DE level for immobilized CGTase on Amberite IRA 900. CD conversion yield of partially cyclized starch with soluble CGTase was higher compared with liquefied one with ${\alpha}-amylase$.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1080-1085
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• 2005

A hybrid control scheme is proposed for the stabilization of backward movement along simple paths for a vehicle composed of a truck and six trailers. The hybrid comprises the combination of a linear quadratic regulator (LQR) and a neurocontroller (NC) that is trained by a genetic algorithm (GA). Acting singly, either the NC or the LQR are unable to perform satisfactorily over the entire range of the operation required, but the proposed hybrid is shown to be capable of providing good overall system performance. The evaluation function of the NC in the hybrid design has been modified from the conventional type to incorporate both the squared errors and the running steps errors. The reverse movement of the trailer-truck system can be modeled as an unstable nonlinear system, with the control problem focusing on the steering angle. Achieving good backward movement is difficult because of the restraints of physical angular limitations. Due to these constraints the system is impossible to globally stabilize with standard smooth control techniques, since some initial states necessarily lead to jack-knife locks. This paper demonstrates that a hybrid of neural networks and LQR can be used effectively for the control of nonlinear dynamical systems. Results from simulated trials are reported.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.5
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• pp.197-203
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• 2008

In this work, we present a novel genetic approach to solve the problem of combining power control and data rate transmission adjustment for the performance enhancement of the next generation CDMA system. We obtained the optimal solution of multi rate and power control problem by compromising slightly on SIR limit values. The proposed algorithm was able to handle many more users with comparable or faster convergent service. While this paper considered two kinds of fitness function such as maximizing the total transmission data rate and maximizing the acceptable mobiles of CDMA cellular network, the evaluation function combining these two cases or others can be also easily implemented. The simulation results showed the effectiveness and validity of our approach.

• Journal of the Korean Geotechnical Society
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• v.21 no.9
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• pp.25-34
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• 2005

This paper proposes a selection methodology composed of neural network (NN) and genetic algorithm (GA) to design optimal NN structure. We combine the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications and increase the precision of NN' prediction in the design of NN structure. Genetic selection approach of design parameters of NN is introduced to obtain optimal NN structure. Analyzed results for geotechnical problems are given to evaluate the performance of the proposed hybrid methodology.

• Structural Engineering and Mechanics
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• v.63 no.2
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• pp.237-249
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• 2017

The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.12
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• pp.1718-1723
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• 2017

Objective: The study explored the dynamics of shearing force and its correlation with chemical compositions and in vitro dry matter digestibility (IVDMD) of stylo. Methods: The shearing force, diameter, linear density, chemical composition, and IVDMD of different height stylo stem were investigated. Linear regression analysis was done to determine the relationships between the shearing force and cut height, diameter, chemical composition, or IVDMD. Results: The results showed that shearing force of stylo stem increased with plant height increasing and the crude protein (CP) content and IVDMD decreased but fiber content increased over time, resulting in decreased forage value. In addition, tall stem had greater shearing force than short stem. Moreover, shearing force is positively correlated with stem diameter, linear density and fiber fraction, but negatively correlated with CP content and IVDMD. Conclusion: Overall, shearing force is an indicator more direct, easier and faster to measure than chemical composition and digestibility for evaluation of forage nutritive value related to animal performance. Therefore, it can be used to evaluate the nutritive value of stylo.

• Journal of KIISE:Software and Applications
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• v.34 no.5
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• pp.455-464
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• 2007

The construction of a fingerprint database is important to evaluate the performance of an automatic fingerprint recognition system. Due to the cost of collecting fingerprints, there are only few benchmark databases available. Since it is hard to evaluate how robust the system is in various environments with the databases, this paper proposes a novel method that generates fingerprint images automatically from only a few training samples by using the genetic algorithm. Fingerprints generated by the proposed method include similar characteristics of those collected from the corresponding real environment. The proposed method has been verified by comparing with real fingerprint images, showing the usefulness of the method.

• Clinical and Experimental Pediatrics
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• v.58 no.11
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• pp.407-414
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• 2015

Early-onset epileptic encephalopathies are one of the most severe early onset epilepsies that can lead to progressive psychomotor impairment. These syndromes result from identifiable primary causes, such as structural, neurodegenerative, metabolic, or genetic defects, and an increasing number of novel genetic causes continue to be uncovered. A typical diagnostic approach includes documentation of anamnesis, determination of seizure semiology, electroencephalography, and neuroimaging. If primary biochemical investigations exclude precipitating conditions, a trial with the administration of a vitaminic compound (pyridoxine, pyridoxal-5-phosphate, or folinic acid) can then be initiated regardless of presumptive seizure causes. Patients with unclear etiologies should be considered for a further workup, which should include an evaluation for inherited metabolic defects and genetic analyses. Targeted next-generation sequencing panels showed a high diagnostic yield in patients with epileptic encephalopathy. Mutations associated with the emergence of epileptic encephalopathies can be identified in a targeted fashion by sequencing the most likely candidate genes. Next-generation sequencing technologies offer hope to a large number of patients with cryptogenic encephalopathies and will eventually lead to new therapeutic strategies and more favorable long-term outcomes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.105-116
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• 2018

In this study, we consider the assembly line balancing (ALB) problem which is known as an very important decision dealing with the optimal design of assembly lines. We consider ALB problems with soft constraints which are expected to be fulfilled, however they are not necessarily to be satisfied always and they are difficult to be presented in exact quantitative forms. In previous studies, most researches have dealt with hard constraints which should be satisfied at all time in ALB problems. In this study, we modify the mixed integer programming model of the problem introduced in the existing study where the problem was first considered. Based on the modified model, we propose a new algorithm using the genetic algorithm (GA). In the algorithm, new features like, a mixed initial population selection method composed of the random selection method and the elite solutions of the simple ALB problem, a fitness evaluation method based on achievement ratio are applied. In addition, we select the genetic operators and parameters which are appropriate for the soft assignment constraints through the preliminary tests. From the results of the computational experiments, it is shown that the proposed algorithm generated the solutions with the high achievement ratio of the soft constraints.