• Journal of Communications and Networks
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• v.13 no.4
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• pp.319-326
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• 2011

Beamforming techniques have been successfully utilized for traffic channels in order to solve the interference problem. However, their use for control channels has not been sufficiently investigated. In this paper, a (semi-) centralized beamforming strategy that adaptively changes beam patterns and controls the total transmit power of cells is proposed for the performance enhancement of the common channel in hierarchical cell structure (HCS) networks. In addition, some examples of its practical implementation with low complexity are presented for two-tier HCS networks consisting of macro and pico cells. The performance of the proposed scheme has been evaluated through multi-cell system-level simulations under optimistic and pessimistic interference scenarios. The cumulative distribution function of user geometry or channel quality has been used as a performance metric since in the case of common control channel the number of outage users is more important than the sum rate. Simulation results confirm that the proposed scheme provides a significant gain compared to the random beamforming scheme as well as conventional systems that do not use the proposed algorithm. Finally, the proposed scheme can be applied simultaneously to several adjacent macro and pico cells even if it is designed primarily for the pico cell within macro cells.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07a
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• pp.33-36
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• 2004

The process modeling for the growth rate in pulsed laser deposition(PLD)-grown ZnO thin films is investigated using neural networks(NNets) and the process recipes is optimized via genetic algorithms(GAs). D-optimal design is carried out and the growth rate is characterized by NNets based on the back-propagation(BP) algorithm. GAs is then used to search the desired recipes for the desired growth rate. The statistical analysis is used to verify the fitness of the nonlinear process model. This process modeling and optimization algorithms can explain the characteristics of the desired responses varying with process conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1426-1447
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• 2021

The payoff game between task publishers and users in the mobile crowdsensing environment is a hot topic of research. A optimal payoff selection model based on stochastic evolutionary game is proposed. Firstly, the process of payoff optimization selection is modeled as a task publisher-user stochastic evolutionary game model. Secondly, the low-quality data is identified by the data quality evaluation algorithm, which improves the fitness of perceptual task matching target users, so that task publishers and users can obtain the optimal payoff at the current moment. Finally, by solving the stability strategy and analyzing the stability of the model, the optimal payoff strategy is obtained under different intensity of random interference and different initial state. The simulation results show that, in the aspect of data quality evaluation, compared with BP detection method and SVM detection method, the accuracy of anomaly data detection of the proposed model is improved by 8.1% and 0.5% respectively, and the accuracy of data classification is improved by 59.2% and 32.2% respectively. In the aspect of the optimal payoff strategy selection, it is verified that the proposed model can reasonably select the payoff strategy.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.314-324
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• 2020

Due to the nonlinearity and environmental uncertainties, the design of the ship's steering controller is a long-term challenge. The purpose of this study is to design an intelligent autopilot based on Extended Kalman Filter (EKF) trained Radial Basis Function Neural Network (RBFNN) control algorithm. The newly developed free running model scaled surface vessel was employed to execute the motion control experiments. After describing the design of the EKF trained RBFNN autopilot, the performances of the proposed control system were investigated by conducting experiments using the physical model on lake and simulations using the corresponding mathematical model. The results demonstrate that the developed control system is feasible to be used for the ship's motion control in the presences of environmental disturbances. Moreover, in comparison with the Back-Propagation (BP) neural networks and Proportional-Derivative (PD) based control methods, the EKF RBFNN based control method shows better performance regarding course keeping and trajectory tracking.

• Computers and Concrete
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• v.28 no.6
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• pp.635-642
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• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• Mood & Emotion
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• v.16 no.3
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• pp.129-133
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• 2018

Objectives : The fourth revision of Korean Medication Algorithm Project for Bipolar Disorder (KMAP-BP) was performed in 2018, to provide newer guidelines for clinicians. In this section, we examined expert opinions to facilitate clinical decisions relative to treating bipolar disorder with medical comorbidity. Methods : The survey was completed by the review committee, consisting of 61 experienced psychiatrists. This part of the survey constitutes treatment strategies, under major medical comorbidities. The executive committee analyzed results, and discussed the final production of algorithm. Results : Aripiprazole was the first-line medication for bipolar patients with metabolic syndrome, cardiovascular, hepatic, renal, and cerebrovascular comorbidities. Ziprasidone also was recommended as the first-line medication in case of metabolic syndrome. Lithium also was regarded as the first-line medication, in case of hepatic problems. Valproate also was considered as the first-line medication, in case of cerebrovascular problems. Conclusion : This study provided the most recent consensus among experts, for treatment of bipolar disorder with physical problems.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.1
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• pp.3-7
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• 2005

The insulin-like growth factors (IGFs), their receptors, and their binding proteins play key roles in regulating cell proliferation and apoptosis. Insulin-like growth factor binding protein-3 (IGFBP3, OMIM #146732) is one of the proteins that bind to the IGFs. IGFBP3 is a modulator of IGF bioactivity, and direct growth inhibitor in the extravascular tissue compartment. We identified twenty-two novel single nucleotide polymorphisms (SNPs) in IGFBP3 gene in Korean cattle (Hanwoo, Bos taurus coreanae) by direct sequencing of full gene including -1,500 bp promoter region. Among the identified SNPs, five common SNPs were screened in 650 Korean cattle; one SNP in promoter (IGFBP3 G-854C), one in 5'UTR region (IGFBP3 G-100A), two in intron 1 (IGFBP3 G+421T, IGFBP3 T+1636A), and one in intron 2 (IGFBP3 C+3863A). The frequencies of each SNP were 0.357 (IGFBP3 G-854C), 0.472 (IGFBP3 G-100A), 0.418 (IGFBP3 G+421T), 0.363 (IGFBP3 T+1636A) and 0.226 (IGFBP3 C+3863A), respectively. Haplotypes and their frequencies were estimated by EM algorithm. Six haplotypes were constructed with five SNPs and linkage disequilibrium coefficients (|D'|) between SNP pairs were also calculated. The information on SNPs and haplotypes in IGFBP3 gene could be useful for genetic studies of this gene.

• Food Science of Animal Resources
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• v.38 no.2
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• pp.362-375
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• 2018

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

• Journal of Sensor Science and Technology
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• v.7 no.6
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• pp.418-425
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• 1998

Stereo cameras are the most widely used sensing systems for automated machines including robots to interact with their three-dimensional(3D) working environments. The position of a target point in the 3D world coordinates can be measured by the use of stereo cameras and the camera calibration is an important preliminary step for the task. Existing camera calibration techniques can be classified into two large categories - linear and nonlinear techniques. While linear techniques are simple but somewhat inaccurate, the nonlinear ones require a modeling process to compensate for the lens distortion and a rather complicated procedure to solve the nonlinear equations. In this paper, a method employing a neural network for the calibration problem is described for tackling the problems arisen when existing techniques are applied and the results are reported. Particularly, it is shown experimentally that by utilizing the function approximation capability of multi-layer neural networks trained by the back-propagation(BP) algorithm to learn the error pattern of a linear technique, the measurement accuracy can be simply and efficiently increased.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.6
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• pp.749-756
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• 2015

In this paper, we propose a unique systolic structure and communication algorithm that maintains a solid link between nodes using synchronous digital communication and enables low power communication. This system was designed by using CC2500 RF transceiver, CC2590 RF front end and C8051F330 low power microcontroller. The measurement of power consumption in the network link shows below $400{\mu}W$ in data transfer rate 320bps. The system constitutes the base unit of low power wireless network that was composed of each seven link nodes having eight sensor nodes. Results of the experiments show that link nodes using a 4Ah battery could operate over 3 years without replacement.