• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.899-907
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• 1996

In this paper, we proposed an algorithm to eliminate false feature points to upgrade the system ability using the cross number method. By using the proposal algorithm, it was able to extract feature points exactly and increase the recognition rates.In the experiment, we used3 groups : the uncut-partially noised, and uncut completely noised fingerprint to verify our proposal. We then compared the results of the proposed system with those of another system in which false feature points were not eliminated. We have obtained good recognition rates of 97.7%, 97.7%, and95.0% in the cut, uncut-partially noised, and the uncut-completely noised fingerprints. However, the other system received the results of the cut 60%, the uncut-partially noised 35%, and the uncut-completely noised 50% respectively. As a result, we belive that the fingerprint may be recognized after adapting the proposal algorithm.

• International Journal of Advanced Culture Technology
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• v.4 no.2
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• pp.19-26
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• 2016

The most important factor in the wireless sensor network is the use of effective energy and increase in lifetime of the individual nodes in order to operate the wireless network more efficiently. For this purpose, various routing protocols have been developed. The LEACH such a protocol, well known among typical cluster routing protocols. However, when a cluster head is selected, the energy consumption may not be equal because it does not take into account the energy of the nodes. In this paper, we seek to improve the cluster head selection method according to residual energy of each sensor node. This method then adaptively applies the LEACH algorithm and the cluster head section algorithm with consideration of node energy in accordance with the energy of the whole sensor field. Through the simulation, it was found that this proposed algorithm was effective.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.505-510
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• 2000

Vehicle performances such as fuel consumption and catalyst-out emissions are affected by a driving pattern, which is defined as a driving cycle with the grade in this study. We developed an algorithm to recognize a current driving pattern by using a neural network. And this algorithm can be used in adapting the driving control strategy to the recognized driving pattern. First, we classified the general driving patterns into 6 representative driving patterns, which are composed of 3 urban driving patterns, 2 suburban driving patterns and 1 expressway driving pattern. A total of 24 parameters such as average cycle velocity, positive acceleration kinetic energy, relative duration spent at stop, average acceleration and average grade are chosen to characterize the driving patterns. Second, we used a neural network (especially the Hamming network) to decide which representative driving pattern is closest to the current driving pattern by comparing the inner products between them. And before calculating inner product, each element of the current and representative driving patterns is transformed into 1 and -1 array as to 4 levels. In the end, we simulated the driving pattern recognition algorithm in a temporary pattern composed of 6 representative driving patterns and, verified the reliable recognition performance.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.163-168
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• 2002

The voting algorithm for action selection performs self-improvement by Reinforcement learning algorithm in the dynamic environment. The proposed voting algorithm improves the navigation of the robot by adapting the eligibility of the behaviors and determining the Command Set Generator (CGS). The Navigator that using a proposed voting algorithm corresponds to the CGS for giving the weight values and taking the reward values. It is necessary to decide which Command Set control the mobile robot at given time and to select among the candidate actions. The Command Set was learnt online by means as Q-learning. Action Selector compares Q-values of Navigator with Heterogeneous behaviors. Finally, real-world experimentation was carried out. Results show the good performance for the selection on command set as well as the convergence of Q-value.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.409-418
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• 2004

LOD is a widely used technique in 3D game and animation to represent large 3D data sets smoothly in real-time. Most LOD algorithms use a binary tree to keep the ancestor information. A new algorithm proposed in this paper, however, do not keep the ancestor information, thus use the less memory space and rather increase the rendering performance. To verify the efficiency of the proposed algorithm, performance comparison with ROAM is conducted in real-time 3D terrain navigation. Result shows that the proposed algorithm uses about 1/4 of the memory space of ROAM and about 4 times faster than ROAM.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.199-206
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• 2004

This paper proposes a hybrid independent component analysis(ICA) of fixed-point(FP) algorithm and robust algorithm. The FP algorithm is applied for improving the analysis speed and performance, and the robust algorithm is applied for preventing performance degradations by means of very small kurtosis and temporal correlations between components. And the adaptive adaptation of temporal correlations has been proposed for solving limits of the conventional robust algorithm dependent on the maximum time delay. The proposed ICA has been applied to the problems for separating the 4-mixed signals of 500 samples and 10-mixed images of $512\times512$pixels, respectively. The experimental results show that the proposed ICA has a characteristics of adaptively adapting the maximum time delay, and has a superior separation performances(speed, rate) to conventional FP-ICA and hybrid ICA of heuristic correlation. Especially, the proposed ICA gives the larger degree of improvement as the problem size increases.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.107-112
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• 2016

This paper proposes the improving the equalization performance using the variable step size in the S-MMA (Sliced-Multi Modulus Algorithm) equalization algorithm in order to minimize the effect of intersymbol interference which occurs at the nonlinear transfer function of communication channel. The S-MMA were showned for the improving the steady state equalization performance and misadjustment compared to the MMA present algorithm, this two algorithm has a limitation of performance improvement due to the adapting the fixed step size according to the error signal amplitude. In order to solving the abovemensioned problem, the proposed algorithm was adopting the variable step size proportional to the error signal amplitude and the computer simulation was performed for showing the performance improving. As a result of simulation, the proposed VSS S-MMA algorithm has more superior equalization performance compared to the present S-MMA.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.813-815
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• 2011

This study inspects the fault of the vehicle forming assembly and the assembly state of components at high speed and high degree of precision. This study also proposes the general inspection system capable of adapting to a number of products. The inspection program is composed of the fault inspection algorithm to examine the surface of the object and the state of the assembly and the high speed procession algorithm for the real time examination. The fault inspection algorithm is processed largely by a method using average of pixel in ROI and a method dividing the area and checking the presence of the object. Lastly, we verified the efficiency of the sysytem through the evaluation of its accuracy and processing time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6B
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• pp.375-381
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• 2007

This paper focuses on the prediction algorithm among the context-awareness technologies. With a representative algorithm, Bayesian Networks, it is difficult to realize a context-aware as well as to decrease process time in real-time environment. Moreover, it is also hard to be sure about the accuracy and reliability of prediction. One of the simplest algorithms is the sequential matching algorithm. We use it by adding the proposed Cache Scheme. It is adequate for a context-aware service adapting user's habit and reducing the processing time by average 48.7% in this paper. Thus, we propose a design method of user location prediction algorithm that uses sequential matching with the cache scheme by taking user's habit or behavior into consideration. The novel approach will be dealt in a different way compared to the conventional prediction algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.93-99
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• 2014

This paper deals with the FC-MMA (Fast Convergence-Multi Modulus Algorithm) which is improving the convergence characteristics of the MMA (Multiple Modulus Algorithm) adaptive equalization algorithm that is used for the minimization of the intersymbol interference which occurs in the time dispersive communication channel. In the time varying charateristics and the abnormal situation like as outage of the communication channel, the adaptive equalizer needs to adapting the new environment more rapidly. For this problem, the residual isi and the maximum distortion performance index which are meaning the convergence characteristics are widely adapted in the adaptive equalizer. The 16-QAM signal is transmitted and it was confirmed that the proposed algorithm, the FC-MMA has the fast convergence performance such as in the 1.75 times fast in residual isi and 2.5 times fast in the maximum distortion in order to reaching the steady state compare to the MMA algorithm in the same channel environment by the computer simulation.