• 한국콘텐츠학회논문지
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• 제21권1호
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• pp.572-578
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• 2021

본 연구는 지체장애인을 대상으로 인공지능 스피커에 대한 인지적 요소와 감정적 요소가 인공지능 스피커 사용 의도에 미치는 영향을 검증하는 것을 목적으로 하였다. 연구방법은 지체장애인을 대상으로 온라인 설문 조사를 실시하였다. 인공지능 스피커에 대한 인지도와 필요도, 인지된 친밀함과 즐거움, 사용 의도를 파악하였으며, 각 변인이 지체장애인의 인공지능 스피커 사용 의도에 미치는 영향력을 확인하기 위해 다중회귀분석(Multiple linear regression analysis)을 실시하였다. 연구 결과, 지체장애인의 인공지능 스피커에 대한 인지된 즐거움은 사용 의도에 유의한 정적 영향을 나타내었다. 그러나 지체장애인의 인공지능 스피커에 대한 인지도와 필요도, 인지된 친밀함은 인공지능 스피커 사용 의도에 통계적으로 유의미한 영향을 나타내지 않는 것으로 분석되었다. 본 연구의 결과는 장애인의 인공지능 스피커 사용 의도 향상을 위해 즐거움 요소를 강화하는 것이 필요함을 시사하며, 장애인을 위한 인공지능 제품과 맞춤형 서비스를 개발하기 위한 기초자료를 제공하는 점에서 의의가 있다.

• 대한의용생체공학회:의공학회지
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• 제18권1호
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• pp.51-64
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• 1997

A dynamic model of the Korean total artificial heart(TAH) which contains a brushless DC motor, all of mechanical components, the pump system with integrated variable volume space(WS) and the circulatory system model including the bronchial circulation were established Two different sets of seven differential equations were separately derived for the left and right systolic period of the Korean TAH operation. Throughout the computer simulation, a full-state fEedback optimal controller that minimizes the power consumption of the Korean TAH and drives the end stage velocity of the energy converter to zero was developed based upon the optimal control theory. Robustness of the controller were also analyzed with the dynamic model of the Korean TAH.

• KIEE International Transactions on Electrophysics and Applications
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• 제3C권1호
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• pp.5-9
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• 2003

The waveforms of partial discharges (PDs) imply physical and structural properties of PD sources, so analyzing them give us information on the kind of PD sources and the location. Waveforms of PD as a time series function have variable amplitudes but sustain a certain uniform shape, which shows well the characteristics of the waveforms and frequency region. They can also be used as parameters having time and frequency information of PD signals and applied to classification of multiple PDs sources via Artificial Neural Network with back propagation (BP) learning.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.159-159
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• 2019

In this study, the simulation of sediment deposition at Sangju weir reservoir, South Korea, was carried out using artificial neural networks. The ANNs have typically been used in water resources engineering problems for their robustness and high degree of accuracy. Three basic variables namely turbid water inflow, outflow, and water stage have been used as input variables. It was found that ANNs were able to establish valid relationship between input variables and target variable of sedimentation. The R value was 0.9806, 0.9091, and 0.8758 for training, validation, and testing phase respectively. Comparative analysis was also performed to find optimum structure of ANN for sediment deposition prediction. 3-14-1 network architecture using BR algorithm outperformed all other combinations. It was concluded that ANN possess mapping capabilities for complex, non-linear phenomenon of reservoir sedimentation.

• Journal of information and communication convergence engineering
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• 제22권2호
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• pp.133-138
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• 2024

Dissolved oxygen (DO) is an important factor in ecosystems. However, the analysis of DO is frequently rather complicated because of the nonlinear phenomenon of the river system. Therefore, a convenient model-free algorithm for DO variable is required. In this study, a data-driven algorithm for predicting DO was developed by combining XGBoost and an artificial neural network (ANN), called ANN-XGB. To train the model, two years of ecosystem data were collected in Anyang, Seoul using the Troll 9500 model. One advantage of the proposed algorithm is its ability to capture abrupt changes in climate-related features that arise from sudden events. Moreover, our algorithm can provide a feature importance analysis owing to the use of XGBoost. The results obtained using the ANN-XGB algorithm were compared with those obtained using the ANN algorithm in the Results Section. The predictions made by ANN-XGB were mostly in closer agreement with the measured DO values in the river than those made by the ANN.

• Journal of Korea Artificial Intelligence Association
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• 제1권2호
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• pp.15-20
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• 2023

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.815-821
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• 2005

Among the various artificial neural networks the backpropagation network (BPN) has become a standard one. One of the components in a neural network is an activating function or a transfer function of which a representative function is a sigmoid. We have discovered that by updating the slope parameter of a sigmoid function simultaneous with the weights could improve performance of a BPN.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.513-515
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• 1999

In this paper, Neuro-Fuzzy controller which has the characteristic of Fuzzy control and artificial Neural Network is designed A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which are created by the expert. In order to adaptivity, the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in Neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of Dual mode Method. To test the effectiveness of the algorithm designed above the experiment for DC servo motor with variable load as variable load plant is implementation.

• 보건행정학회지
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• 제12권3호
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• pp.54-76
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• 2002

This study investigated how much EVA which evaluate firm's value can explain hospital bankruptcy prediction as a explanatory variable including financial indicators in Korea. In this study, artificial neural network and logit regression which are traditional statistical were used as the model for bankruptcy prediction. Data used in this study were financial and economic value added indicators of 34 bankrupt and -:4 non-bankrupt hospitals from the Database of Korean Health Industry Development Institute. The main results of this study were as follows: First, there was a significant difference between the financial variable model including EVA and the financial variable model excluding EVA in pre-bankruptcy analysis. Second, EVA could forecast bankruptcy hospitals up to 83% by the logistic analysis. Third, the EVA model outperformed the financial model in terms of the predictive power of hospital bankruptcy. Fourth, The predictive power of neural network model of hospital bankruptcy was more powerful than the legit model. After all the result of this study will be useful to future study on EVA to evaluate bankruptcy hospitals forecast.

• 오토저널
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• 제15권1호
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• pp.81-88
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• 1993

A direct treatment of the min-max type objective function of the dynamic response optimization problem is proposed. Previously, the min-max type objective function was transformed to an artificial design variable and an additional point-wise state variable constraint function was imposed, which increased the complexity of the optimization problem. Especially, the design sensitivity analysis for the augmented Lagrangian functional with the suggested treatment is established by using the adjoint variable method and a computer program to implement the proposed algorithm is developed. The optimization result of the proposed treatment are obtained for three typical problems and compared with those of the previous treatment. It is concluded that the suggested treatment in much more efficient in the computational effort than the previous treatment with giving the similar optimal solutions.