• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.55-62
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• 2006

This thesis describes collision avoidance using fuzzy logic based on "Right of way" rules of ICAO and FAA and pilot's experiences for Unmanned Aerial Vehicle(UAV). To apply the rules, we designed fuzzy logic based collision avoidance system. And we also designed decision logic for enable condition of collision avoidance system. Decision logic have three kinds of core key, i.e. Relative Range, Time of CPA(Closest Point of Approach) and Distance at CPA. Application of decision logic made a possible to avoid NMAC(Near Mid-Air Collision) and it has been verified through several simulations. To conclude, we proposed the method to carry out "See and Avoid" ability on UAVs, which is capability to mingle with manned aircraft in civil airspace.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1495-1504
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• 2008

New license plates have been used since 2007. This paper proposes a new license plate extraction method using a gray labeling and a fuzzy reasoning method. First, the proposed method extracts the candidate plates by the gray labeling which is the enhanced version of a non-recursive flood-filling algorithm. By newly designed fuzzy inference system. fitness of each candidate plates are calculated. Finally, the area of the license plate in a image is extracted as a region of the candidate label which has the highest fitness. In the experiments, various license plate images took from indoor/outdoor parking lot, street, etc. by digital camera or cellular phone were used and the proposed extraction method was showed remarkable results of a 94 percent success.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.366-373
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• 2017

In this paper, we propose a variable weighted average (VWA) learning method in order to improve the performance of the fuzzy ART neural network that has been developed by Grossberg. In a conventional method, the Fast Commit Slow Recode (FCSR), when an input pattern falls in a category, the representative pattern of the category is updated at a fixed learning rate regardless of the degree of similarity of the input pattern. To resolve this issue, a variable learning method proposes reflecting the distance between the input pattern and the representative pattern to reduce the FCSR's category proliferation issue and improve the pattern recognition rate. However, these methods still suffer from the category proliferation issue and limited pattern recognition rate due to inevitable excessive learning created by use of fuzzy AND. The proposed method applies a weighted average learning scheme that reflects the distance between the input pattern and the representative pattern when updating the representative pattern of a category suppressing excessive learning for a representative pattern. Our simulation results show that the newly proposed variable weighted average learning method (VWA) mitigates the category proliferation problem of a fuzzy ART neural network by suppressing excessive learning of a representative pattern in a noisy environment and significantly improves the pattern recognition rates.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.2
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• pp.106-110
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• 2014

This paper proposed the face recognition method which can be effectively applied to the robot's vision system. The proposed algorithm is recognition using hue extraction and feature point. hue extraction was using difference of skin color, pupil color, lips color. Features information were extraction from eye, nose and mouth using feature parameters of the difference between the feature point, distance ratio, angle, area. Feature parameters fuzzified data with the data generated by membership function, then evaluate the degree of similarity was the face recognition. The result of experiment are conducted with frontal color images of face as input images the received recognition rate of 96%.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.287-292
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• 2003

The ability of robot is being various and complex. The robot is utilizing distance, image data and voice data for sensing its circumstance. This paper suggests the 2-layer fuzzy control as the algorithm that control robot with various sensor information. In a obstacle avoidance, it utilizes many range finders and classifies them into 3parts(front, left, right). In 3 sub-controllers, the controller executes fuzzy conference. And then it executes combined control with a combination of outputs of 3 sub-controllers in the second step. The text compares the 2-layer fuzzy controller with the hierarchical fuzzy controller that has analogous structure. And the performance of the 2-layer fuzzy controller is confirmed by application this controller to robot following, simulation to each other and real experiment.

• The KIPS Transactions:PartC
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• v.14C no.7
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• pp.611-620
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• 2007

Distributing the routing path over the entire network is an important factor to maintain the lifetime of wireless sensor network as long as possible. This paper proposes a fuzzy routing protocol that decides a routing path based on the fuzzy control rules. The fuzzy controller receives the energy values, distances, and hop counts of possible route paths as input, and the inference engine produces the contribution factors for each of route paths. The route path with the largest contribution factor is elected as the final routing path. The nodes contained in the routing path reduce their energy after transmitting a data packet so as to prevent the same route path from being selected repeatedly. It makes the network traffic spreaded over the network resulting longer network lifetime. The computer simulations on TinyOS have shown that the fuzzy routing protocol is more energy efficient and has longer network lifetime compared to the existing routing protocols.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.41-47
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• 2008

Cluster based fuzzy model tree has certain drawbacks to decrease performance of testinB data when over-fitting of training data exists. To reduce the sensitivity of performance due to over-fitting problem, we proposed a modified cluster based fuzzy model tree with node information. To construct model tree, cluster centers are calculated by fuzzy clustering method using all input and output attributes in advance. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. In the prediction step, membership values are calculated by using fuzzy distance between input attributes and all centers that passing the nodes from root to leaf nodes. Finally, data prediction is performed by the weighted average method with the linear models and fuzzy membership values. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional cluster based fuzzy model tree.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.83-93
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• 2012

This paper proposes an image segmentation framework that modifies the objective function of Fuzzy C-Means (FCM) to improve the performance and computational efficiency of the conventional FCM-based image segmentation. The proposed image segmentation framework includes a locally weighted fuzzy c-means (LWFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors. Distance between a center pixel and a neighboring pixels are calculated within a window and these are basis for determining weights to indicate the importance of the memberships as well as to improve the clustering performance. We analyzed the segmentation performance of the proposed method by utilizing four eminent cluster validity functions such as partition coefficient ($V_{pc}$), partition entropy ($V_{pe}$), Xie-Bdni function ($V_{xb}$) and Fukuyama-Sugeno function ($V_{fs}$). Experimental results show that the proposed LWFCM outperforms other FCM algorithms (FCM, modified FCM, and spatial FCM, FCM with locally weighted information, fast generation FCM) in the cluster validity functions as well as both compactness and separation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.11
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• pp.1429-1436
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• 2011

In this paper, a new tube-radius-measuring algorithm has been proposed for effectively measuring the radii of small tubes under severe noise conditions that can also perform well when metal scraps that make it difficult to measure the radius correctly are inside the tube hole. In the algorithm, we adopt a fuzzy searching method that searches for the center of the inner circle by using fuzzy parameters for distance and orientation from the initial search point. The proposed algorithm has been implemented and tested on both synthetic and real-world tube images, and the performance is compared to existing circle-detection algorithms, such as the Hough transform and RANSAC methods, to prove the accuracy and effectiveness of the algorithm. From this comparison, it is concluded that the proposed algorithm has excellent performance in terms of measurement accuracy and computation time.

• Journal of Sensor Science and Technology
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• v.12 no.2
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• pp.79-87
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• 2003

In this paper, we propose a method to obtain a linear characteristic using soft computing for systems which have array sensors of nonlinear characteristics. Also a procedure utilizing the pattern information of array sensors without additional sensors is proposed to reduce disturbance effects. For a typical example, even a single CdS cell for CdS array has nonlinear characteristics. Overall linear characteristic for CdS array is obtained using fuzzy logic for each cell and overlapped portion. In addition, further improvement for linearization is obtained applying genetic algorithms for the parameters of membership functions. Also the effect of disturbing external light changes to the CdS array can be reduced without using any additional sensors for calibration. The proposed method based on fuzzy logic shows improvements for position measurements and disturbance reduction to external light changes due to the fuzziness of the shadow boundary as well as the inherent nonlinearity of the CdS array. This improvement is shown by applying the proposed method to the ball position measurements of a magnetic levitation system.