• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.148-153
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• 2007

In this paper, the evolutionary adaptive wavelet transform(EAWT) is applied to the scattering analysis of radar target. EAWT algorithm uses evolutionary programming for the time-frequency parameter extraction instead of FFT and the bisection search method used in the conventional adaptive wavelet transform(AWT). Therefore, the EAWT has a better performance than the conventional AWT. In the simulation using wire target(Airbus-like), the comparisons with the conventional AWT are presented to show the superiority of the EAWT algorithm in the analysis of scattering phenomenology. The EAWT can be effectively applied to the radar target recognition.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.56-58
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• 2004

In this paper, we propose an adaptive beamforming algorithm for array antenna. The proposed beamforming algorithm is based on EDS (Euclidean Direction Search) algorithm. Generally LMS algorithm has a much slower rate of convergence, but its low computational complexity and robustness make it a representative method of adaptive beamforming. Although the RLS algorithm is known for its fast convergence to the optimal Wiener solution, it still suffers from high computational complexity and poor performance. The proposed EDS algorithm has a rapid convergence better than LMS algorithm, and has a computational more simple complexity than RLS algorithm. In this paper we compared the efficiency of the EDS algorithm with a standard LMS algorithm.

• The KIPS Transactions:PartC
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• v.14C no.5
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• pp.425-430
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• 2007

Without the aid of stationary infrastructure, maintaining routing information for all nodes is inefficient in the Mobile Ad hoc Networks(MANET). It is more efficient when every time routing information is necessary that the source node broadcasts a query message to neighbour nodes. The source node using Ad hoc On-Demand distance Vector(AODV), which is one of the routing protocols of MANET, uses the Expanding Ring Search(ERS) algorithm which finds a destination node efficiently. In order to reduce the congestion of the network, ERS algorithm does not broadcast Route REQuest(RREQ) messages in the whole network. When the timer expires, if source node does not receive Route REPly(RREP) messages from the destination node, it gradually increases TTL value and broadcasts RREQ messages. Existing AODV cost a great deal to find a destination node because it uses a fixed NODE_TRAVERSAL_TIME value. Without the message which is added in existing AODV protocols, this paper measures delay time among the neighbours' nodes by making use of HELLO messages. We propose Adaptive ERS(AERS) algorithm that makes NET_TRAVERSAL_TIME optimum which apply to the measured delay time to NODE_TRAVERSAL_TIME. AERS suppresses the unnecessary messages, making NET_TRAVERSAL_TIME optimum in this paper. So we will be able to improve a network performance. We prove the effectiveness of the proposed method through simulation.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.389-395
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• 2015

According to much research on cognitive science, the impact of the scene-context on human visual search in real-world images could be as important as the saliency. Therefore, this study proposed a method of Adaptive Control of Thought-Rational (ACT-R) modeling of visual search in real-world images, based on saliency and scene-context. The modeling method was developed by using the utility system of ACT-R to describe influences of saliency and scene-context in real-world images. Then, the validation of the model was performed, by comparing the data of the model and eye-tracking data from experiments in simple task in which subjects search some targets in indoor bedroom images. Results show that model data was quite well fit with eye-tracking data. In conclusion, the method of modeling human visual search proposed in this study should be used, in order to provide an accurate model of human performance in visual search tasks in real-world images.

• Journal of the Korea Computer Industry Society
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• v.5 no.5
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• pp.755-760
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• 2004

In this paper, an advanced successive elimination algorithm was proposed using mean absolute difference of neighboring search points. By using mean absolute difference of neighboring search points, the search point in motion estimation can be eliminated effeciently without matching evaluation that requires very intensive computations. By using adaptive MAD calculation algorithm, the candidate matching block can be eliminated early. So, the number of the proposed algrorithm was verified by experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3041-3063
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• 2018

Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric. This problem has recently drawn intense research interest. However, most metric-based algorithms tend to include irrelevant subgraphs in the identified community. Apart from the user-defined metric algorithm, how can we search the natural community that the query node belongs to? In this paper, we propose a novel community search algorithm based on the concept of the k-hop and local distance dynamics model, which can naturally capture a community that contains the query node. The basic idea is to envision the nodes that k-hop away from the query node as an adaptive local dynamical system, where each node only interacts with its local topological structure. Relying on a proposed local distance dynamics model, the distances among nodes change over time, where the nodes sharing the same community with the query node tend to gradually move together, while other nodes stay far away from each other. Such interplay eventually leads to a steady distribution of distances, and a meaningful community is naturally found. Extensive experiments show that our community search algorithm has good performance relative to several state-of-the-art algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.73-78
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• 2004

By modeling the block matching algorithm as a function of the correlation of image blocks, we derive search patterns for fast block matching motion estimation. The proposed approach provides an analytical support lot the diamond-shape search pattern, which is widely used in fast block matching algorithms. We also propose a new fast motion estimation algorithm using adaptive search patterns and statistical properties of the object displacement. In order to select an appropriate search pattern, we exploit the relationship between the motion vector and the block differences. By changing the search pattern adaptively, we improve motion prediction accuracy while reducing required computational complexity compared to other fast block matching algorithms.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.437-444
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• 2012

In this paper, we propose a method for predicting a user's location based on their past movement patterns. There is no restriction on the length of past movement patterns when using this method to predict the current location. For this purpose, a modified search tree has been devised. The search tree is constructed in an effective manner while it additionally learns the movement patterns of a user one by one. In fact, the time complexity of the learning process for a movement pattern is linear. In this process, the search tree expands to take into consideration more details about the movement patterns when a pattern that conflicts with an existing trained pattern is found. In this manner, the search tree is trained to make an exact matching, as needed, for location prediction. In the experiments, the results showed that this method is highly accurate in comparison with more complex and sophisticated methods. Also, the accuracy deviation of users of this method is significantly lower than for any other methods. This means that this method is highly stable for the variations of behavioral patterns as compared to any other method. Finally, 1.47 locations were considered on average for making a prediction with this method. This shows that the prediction process is very efficient.

• Journal of Power Electronics
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• v.16 no.1
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• pp.287-296
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• 2016

Conventional maximum power point tracking (MPPT) methods are ineffective under partially shaded conditions because multiple local maximum can be exhibited on power-voltage characteristic curve. This study proposes an improved cuckoo search (ICS) MPPT method after investigating the cuckoo search (CS) algorithm applied in solving multiple MPPT. The algorithm eliminates the random step in the original CS algorithm, and the conception of low-power, high-power, normal and marked zones are introduced. The adaptive step adjustment is also realized according to the different stages of the nest position. This algorithm adopts the large step in low-power and marked zones to reduce search time, and a small step in high-power zone is used to improve search accuracy. Finally, simulation and experiment results indicate that the promoted ICS algorithm can immediately and accurately track the global maximum under partially shaded conditions, and the array output efficiency can be improved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.28-36
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• 2009

In this paper, we propose a frame-rate up-conversion method for temporal quality enhancement. The proposed method adaptively changes search range during hierarchical motion estimation and reconstructs hole regions using the proposed bi-direction prediction and linear interpolation. In order to alleviate errors due to inaccurate motion vector estimation, search range is adaptively changed based on reliability and for more accurate, motion estimation is performed in descending order of block variance. After segmentation of background and object regions, for filling hole regions, the pixel values of background regions are reconstructed using linear interpolation and those of object regions are compensated based on the proposed hi-directional prediction. The proposed algorithm is evaluated in terms of PSNR with original uncompressed sequences. Experimental results show that the proposed algorithm is better than conventional methods by around 2dB, and blocky artifacts and blur artifacts are significantly diminished.