• Journal of Information Processing Systems
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• v.18 no.5
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• pp.711-718
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• 2022

In this paper, an accurate segmentation algorithm of video dynamic background image (VDBI) based on improved wavelet transform is proposed. Based on the smooth processing of VDBI, the traditional wavelet transform process is improved, and the two-layer decomposition of dynamic image is realized by using two-dimensional wavelet transform. On the basis of decomposition results and information enhancement processing, image features are detected, feature points are extracted, and quantum ant colony algorithm is adopted to complete accurate segmentation of the image. The maximum SNR of the output results of the proposed algorithm can reach 73.67 dB, the maximum time of the segmentation process is only 7 seconds, the segmentation accuracy shows a trend of decreasing first and then increasing, and the global maximum value can reach 97%, indicating that the proposed algorithm effectively achieves the design expectation.

• Journal of the Korean Magnetics Society
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• v.7 no.6
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• pp.314-320
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• 1997

We have observed the orientation behavior of acicular iron oxides in magnetic layer of the particular magnetic tape after magnetic paint was coated on polyester film. As results of the static orientation, orientation of iron oxides come to the maximum at the front and the back of orientation magnets, but it was a litter lower at the back than at the front, and it slowly decreases and come to the constant level after passing through the orientation magnets. In case of edge portion to be futher away from center in the coated film, the orientation of iron oxides in magnetic layer come to worse than that of the center. We think it is owing to the shape and the magnetic magnitude of orientation magnets. The results of the dynamic orientation are as follows. As the orientation of iron oxides in the particulated magnetic tape is higher, the output properties of tape come to better than ever. And the orientation of iron oxides can be changed by drying condition, as result, the output properties of tape can be also. Therefore we think the considrations of the design of orientation magnets and the control of drying condition are needed to improve output properties of the particulated magnetic tape.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.33-38
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• 2014

The FCM based hybrid RBF network is a heterogeneous learning network model that applies FCM algorithm between input and middle layer and applies Max_Min algorithm between middle layer and output. The Max-Min neural network uses winner nodes of the middle layer as input but shows inefficient learning in performance when the input vector consists of too many patterns. To overcome this problem, we propose a dynamic learning rate control based on fuzzy logic. The proposed method first classifies accurate/inaccurate class with respect to the difference between target value and output value with threshold and then fuzzy membership function and fuzzy decision logic is designed to control the learning rate dynamically. We apply this proposed RBF network to the character recognition problem and the efficacy of the proposed method is verified in the experiment.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.471-476
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• 2004

Recently, provides information which is most necessary to the user the research against the web information recommendation system for the Internet shopping mall is actively being advanced. the back which it will drive in the object. In that Dynamic Web Recommendation Method Using SOM (Self-Organizing Feature Maps) has the advantages of speedy execution and simplicity but has the weak points such as the lack of explanation on models and fired weight values for each node of the output layer on the established model. The method proposed in this study solves the lack of explanation using the Bayesian reasoning method. It does not give fixed weight values for each node of the output layer. Instead, the distribution includes weight using Hybrid SOM. This study designs and implements Dynamic Web Recommendation Method Using Hybrid SOM. The result of the existing Web Information recommendation methods has proved that this study's method is an excellent solution.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.382-387
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• 2012

Water balance has a significant impact on the overall fuel cell system performance. Proper water management should provide an adequate membrane hydration and avoidance of water flooding in the catalyst layer and gas diffusion layer. Considering the important of advanced water management in PEM fuel cell, this study proposes a simple one dimensional water transportation model of PEM fuel cell for use in a dynamic condition. The model has been created by assumption that the output is the water liquid saturation difference. The liquid saturation change is the total difference between the additional water and the removal water on the system. The water addition is obtained from fuel cell reaction and the electro osmotic drag. The water removal is obtained from capillary transport and evaporation process. The result shows that the capillary water transport of low temperature fuel cell is high because the evaporation rate is low.

• ETRI Journal
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• v.31 no.5
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• pp.601-603
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• 2009

This letter presents a high performance 2.4 GHz two-stage power amplifier (PA) operating in the temperature range from $-30^{\circ}C$ to $+85^{\circ}C$ for IEEE 802.11g, wireless local area network application. It is implemented in InGaP/GaAs hetero-junction bipolar transistor technology and has a bias circuit employing a temperature compensation technique for error vector magnitude (EVM) performance. The technique uses a resistor made with a base layer of HBT. The design improves EVM performance in cold temperatures by increasing current. The implemented PA has a dynamic EVM of less than 4%, a gain of over 26 dB, and a current less than 130 mA below the output power of 19 dBm across the temperature range from $-30^{\circ}C$ to $+85^{\circ}C$.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.120-127
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• 1995

A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1682-1688
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• 2008

The Counterpropagation algorithm(CP) is a combination of Kohonen competition network as a hidden layer and the outstar structure of Grossberg as an output layer. CP has been used in many real applications for pattern matching, classification, data compression and statistical analysis since its learning speed is faster than other network models. However, due to the Kohonen layer's winner-takes-all strategy, it often causes instable learning and/or incorrect pattern classification when patterns are relatively diverse. Also, it is often criticized by the sensitivity of performance on the learning rate. In this paper, we propose an enhanced CP that has multiple Kohonen layers and dynamic controlling facility of learning rate using the frequency of winner neurons and the difference between input vector and the representative of winner neurons for stable learning and momentum learning for controlling weights of output links. A real world application experiment - pattern recognition from passport information - is designed for the performance evaluation of this enhanced CP and it shows that our proposed algorithm improves the conventional CP in learning and recognition performance.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Journal of the Korean Solar Energy Society
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• v.32 no.5
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• pp.118-123
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• 2012

Water balance has a significant impact on the overall fuel cell performance. Maintenance of proper water management should provide an adequate membrane hydration and avoidance of water flooding in the catalyst layer and gas diffusion layer. Considering the important of advanced water management in PEM fuel cell, this study proposes a simple one dimensional water transportation model of PEM fuel cell for use in a dynamic condition. The model has been created by assumption that the output is the water liquid saturation difference. The liquid saturation change is the total difference between the additional water and the removal water on the system. The water addition is obtained from fuel cell reaction and the electro osmotic drag. The water removal is obtained from capillary transport and evaporation process. The result shows that the capillary water transport of low temperature fuel cell is high because the evaporation rate is low.