• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2005년도 추계학술대회 논문집
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• pp.231-234
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• 2005

The high temperature deformation behavior of AZ 31 Mg alloy was investigated by designing a back propagation neural network that uses a gradient descent-learning algorithm. A neural network modeling is an intelligent technique that can solve non-linear and complex problems by learning from the samples. Therefore, some experimental data have been firstly obtained from continuous compression tests performed on a thermo-mechanical simulator over a range of temperatures $(250-500^{\circ}C)$ with strain rates of $0.0001-100s^{-1}$ and true strains of 0.1 to 0.6. The inputs for neural network model are strain, strain rate, and temperature and the output is flow stress. It was found that the trained model could well predict the flow stress for some experimental data that have not been used in the training. Workability of a material can be evaluated by means of power dissipation map with respect to strain, strain rate and temperature. Power dissipation map was constructed using the flow stress predicted from the neural network model at finer Intervals of strain, strain rates and subsequently processing maps were developed for hot working processes for AZ 31 Mg alloy. The safe domains of hot working of AZ 31 Mg alloy were identified and validated through microstructural investigations.

• Journal of Communications and Networks
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• 제9권2호
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• pp.112-117
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• 2007

In this paper, we focus on the total transmission power minimization problem for downlink beamforming multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems while ensuring each user's QoS requirement. Although the linear integer programming (LIP) solution we formulate provides the performance upper bound of the margin adaptive (MA) optimization problem, it is hard to be implemented in practice due to its high computational complexity. By regarding each user's equivalent channel gain as approximate independent values and using iterative descent method, we present a heuristic MA resource allocation algorithm. Simulation results show that the proposed algorithm efficiently converges to the local optimum, which is very close to the performance of the optimal LIP solution. Compared with existing space division multiple access (SDMA) OFDM systems with or without adaptive resource allocation, the proposed algorithm achieves significant performance improvement by exploiting the frequency diversity and multi-user diversity in downlink multiple-input single-output (MISO) OFDM systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.2027-2046
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• 2024

Reconfigurable Intelligent Surface (RIS) is an innovative technique to precisely control the phase of incident signals with the help of low-cost passive reflective elements. It shows excellent potential in the sixth generation of mobile communication systems, which not only extends wireless coverage but also boosts channel capacity. Considering that multipath propagation and a high number of antennas are involved in RIS in assisted mega multiple-input multiple-output (MIMO) systems, it suffers from severe channel fading and multipath effects, which in turn lead to signal instability and degradation of transmission performance. To overcome this obstacle, this essay suggests an improved gradient optimization algorithm to dynamically and optimally adjust the phase of the reflective elements to counteract channel fading and multipath effects as a strategy. In order to overcome the optimization problem of falling into local minima, this paper proposes an adaptive learning rate algorithm based on Adagrad improvement, which searches for the global optimal solution more efficiently and improves the robustness of the optimization algorithm. The suggested technique helps to enhance the estimate of channel efficiency of RIS-assisted large MIMO systems, according to simulation results.

• 대한의용생체공학회:의공학회지
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• 제18권4호
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• pp.485-491
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• 1997

본 논문은 각성상태에 다른 생리신호와 행위신호 및 주관적 평가의 상관관계에 대하여 나타내었다. Nz와 반응시간은 mKSS level 의 변화와 동일한 경향을 나타내는데 반하여 1분당 눈 깜박임 수는 앞의 두 가지 변수와 다른 경향을 나타내었다. 1분당 눈깜박임 수는 mKSS level 1에서 5까지는 낮은 변화율 갖고 mKSS level 7에서는 높은 변화율을 갖는 반면에 mKSS level 9에서는 이와 반대로 변화율이 급격히 감소한다. 피검자들은 서로다른 1분당 눈깜박임 수(EBR)를 가지나 EBR의 변화율은 비슷하였다. 그러므로 EBR의 변화율을 각성판정지표로 사용할 수 있음을 알 수 있었다. 반응시간 실험 결과로부터mKSS level 5이상부터 작업수행능력이 낮아짐을 알 수 있었고 false positive 와 false negative 가 mKSS level3부터 관찰되었으므로 효과적으로 각성제어를 위하여 mKSS level 3과 5사이에 각성상태를 향상시키기 위한 소리나 향기 등의 자극을 주어야 함을 알 수 있었다.

• 전기학회논문지
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• 제61권5호
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• 문화기술의 융합
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• 제10권4호
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• pp.589-594
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• 2024

본 연구는 LTE-Cat.M1 모듈을 사용하여 Sounding Rocket LTE 통신 시험 결과를 소개한다. 개발된 LTE 데이터 송수신 시스템은 임무탑재장비와 지상관측장비로 구성되며, 10 Hz의 속도로 일정하게 임무탑재장비로부터 데이터를 송신하여 지상관측장비에서 수신 시 계측되는 데이터 사이 시간을 바탕으로 지연율을 확보하였다. 실제 비행시험의 정확도를 높이기 위하여 지상 네트워크 지연율 시험과 하드웨어 내부 지연율 시험, 지상 시험을 수행하였다. 비행 시험 결과 상승 단계에서 핸드오버에 실패하여 13초간 통신이 유실되었음을 확인하였고, 이후 낙하산이 전개되어 일정한 위치 변위를 가진 상황에서 통신이 다시 연결됨을 확인하였다. 최종적으로 LTE-Cat.M1 기술은 Sounding Rocket 임무 중 하강 단계 관측 임무나 데이터 백업에 사용할 수 있을 것으로 기대한다.

• Asian-Australasian Journal of Animal Sciences
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• 제21권5호
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• pp.617-623
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• 2008

A fine-mapping method exploiting linkage disequilibrium was used to detect quantitative trait loci (QTL) on the X chromosome affecting milk production, body conformation and productivity traits. The pedigree comprised 22 paternal half-sib families of Black-and-White Holstein bulls in the Netherlands in a grand-daughter design for a total of 955 sons. Twenty-five microsatellite markers were genotyped to construct a linkage map on the chromosome X spanning 170 Haldane cM with an average inter-marker distance of 7.1 cM. A covariance matrix including elements about identical-by-descent probabilities between haplotypes regarding QTL allele effects was incorporated into the animal model, and a restricted maximum-likelihood method was applied for the presence of QTL using the LDVCM program. Significance thresholds were obtained by permuting haplotypes to phenotypes and by using a false discovery rate procedure. Seven QTL responsible for conformation types (teat length, rump width, rear leg set, angularity and fore udder attachment), behavior (temperament) and a mixture of production and health (durable prestation) were detected at the suggestive level. Some QTL affecting teat length, rump width, durable prestation and rear leg set had small numbers of haplotype clusters, which may indicate good classification of alleles for causal genes or markers that are tightly associated with the causal mutation. However, higher maker density is required to better refine the QTL position and to better characterize functionally distinct haplotypes which will provide information to find causal genes for the traits.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.408-421
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• 2015

Because the wheel of V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming job. In order to overcome difficulties for design of the linear controllers, a hybrid recurrent Chebyshev neural network (NN) control system is proposed to control for a PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Chebyshev NN control system consists of an inspector control, a recurrent Chebyshev NN control with adaptive law and a recouped control. Moreover, the online parameters tuning methodology of adaptive law in the recurrent Chebyshev NN can be derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, the optimal learning rate of the parameters based on discrete-type Lyapunov function is derived to achieve fast convergence. The recurrent Chebyshev NN with fast convergence has the online learning ability to respond to the system's nonlinear and time-varying behaviors. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.30-33
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• 2002

To obtain good performance of radial basis function (RBF) neural networks, it needs very careful consideration in design. The selection of several parameters such as the number of centers and widths of the radial basis functions must be considered carefully since they critically affect the network's performance. We propose a learning algorithm for growing of complexity of RBF neural networks which is adapted automatically according to the complexity of tasks. The algorithm generates a new basis function based on the errors of network, the percentage of decreasing rate of errors and the nearest distance from input data to the center of hidden unit. The RBF's center is located at the point where the maximum of absolute interference error occurs in the input space. The width is calculated based on the standard deviation of distance between the center and inputs data. The steepest descent method is also applied for adjusting the weights, centers, and widths. To demonstrate the performance of the proposed algorithm, general problem of function estimation is evaluated. The results obtained from the simulation show that the proposed algorithm for RBF neural networks yields good performance in terms of convergence and accuracy compared with those obtained by conventional multilayer feedforward networks.

• Journal of Microbiology and Biotechnology
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• 제20권4호
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• pp.835-843
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• 2010

The objectives of this study were to investigate the effect of fermentation medium on the hydroxyl radical scavenging activity of exopolysaccharides from Inonotus obliquus by response surface methodology (RSM). A two-level fractional factorial design was used to evaluate the effect of different components of the medium. Corn flour, peptone, and $KH_2PO_4$ were important factors significantly affecting hydroxyl radical scavenging activity. These selected variables were subsequently optimized using path of steepest ascent (descent), a central composite design, and response surface analysis. The optimal medium composition was (% w/v): corn flour 5.30, peptone 0.32, $KH_2PO_4$ 0.26, $MgSO_4$ 0.02, and $CaCl_2$ 0.01. Under the optimal condition, the hydroxyl radical scavenging rate (49.4%) was much higher than that using either basal fermentation medium (10.2%) and single variable optimization of fermentation medium (35.5%). The main monosaccharides components of the RSM optimized polysaccharides are rhamnose, arabinose, xylose, mannose, glucose, and galactose with molar proportion at 1.45%, 3.63%, 2.17%, 15.94%, 50.00%, and 26.81%.