• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.203-210
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• 2000

In this paper, we propose a new unsupervised learning network and competitive learning algorithm for pattern classification. The proposed network is based on relative similarity, which is similarity measure between input data and cluster group. So, the proposed network and algorithm is called relative similarity network(RSN) and learning algorithm. According to definition of similarity and learning rule, structure of RSN is designed and pseudo code of the algorithm is described. In general pattern classification, RSN, in spite of deletion of learning rate, resulted in the identical performance with those of WTA, and SOM. While, in the patterns with cluster groups of unclear boundary, or patterns with different density and various size of cluster groups, RSN produced more effective classification than those of other networks.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.218-223
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• 2010

Discussion and analysis about relative mutual information has been carried out through fuzzy entropy and similarity measure. Fuzzy relative mutual information measure (FRIM) plays an important part as a measure of information shared between two fuzzy pattern vectors. This FRIM is analyzed and explained through similarity measure between two fuzzy sets. Furthermore, comparison between two measures is also carried out.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.76-81
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• 1999

The degree of relative similarity between 2D patterns is obtained using Open-Ball Scheme. Open-Ball Scheme employs a method of transforming the geometrical information on 3D objects or 2D patterns into the features to measure the relative similarity for object(patten) recognition, with invariance on scale, rotation, and translation. The feature of an object is used to obtain the relative similarity and mapped into [0, 1] the interval of real line. For decades, Moment-Invariant Method has been used as one of the excellent methods for pattern classification and object recognition. Open-Ball Scheme uses the geometrical structure of patterns while Moment Invariant Method uses the statistical characteristics. Open-Ball Scheme is compared to Moment Invariant Method with respect to the way that it interprets two-dimensional patten classification, especially the paradigms are compared by the degree of closeness to human's intuitive understanding. Finally the effectiveness of the proposed Open-Ball Scheme is illustrated through simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.66-69
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• 1997

In this paper a fuzzy similarity measure is proposed. The proposed fuzzy similarity measure considers the relative distance between data and cluster centers in addition to the Euclidean distance to decide the degree of similarity. The boundary of a cluster center is constracted on the competitive region and expanded on the less competitive region. This result shows the possibility of using relative distance as a similarity measure.

• Korean Management Science Review
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• v.28 no.1
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• pp.61-74
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• 2011

Not only the preference data but also the similarity data can be used for developing effective marketing strategies. Hahn et al.[10] proposes a methodology of representing a brand(focal brand)'s competitors in a single map called the Preference-Similarity Map, according to their relative preference to and similarity with the focal brand. They also proposes a way to derive the relative preference and similarity values from the survey collecting the choice data from differing choice sets. This study identifies the limitations of the preference and similarity measures proposed by Hahn et al.[10] and shows how these measures can be revised. This study also proposes how to implement the revised measures and analyze brands' positioning strategies. Based on the results of the previous studies on the effect of inter brand similarity on brand evaluations, this study assumes that it is important to analyze how much a specific brand is preferred to its close competitors when evaluating the effectiveness of the brand's positioning in the market. This study applies the proposed measures to the data used in Hahn et al.[10] and also show how the proposed measures are related to the parameters of the choice model proposed by Batsell and Polking[1].

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.350-353
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• 2001

In general, the Least Square Error method is used for signal classification to measure distance in the $l^2$ metric or the $L^2$ metric space. A defect of the Least Square Error method is that it does not classify properly some waveforms, which is due to the property of the Least Square Error method: the global analysis. This paper proposes a new linear operator, the Integra-Normalizer, that removes the problem. The Integra-Normalizer possesses excellent property that measures the degree of relative similarity between signals by expanding the functional space with removing the restriction on the functional space inherited by the Least Square Error method. The Integra-Normalizer shows superiority to the Least Square Error method in measuring the relative similarity among one dimensional waveforms.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• Journal of Korea Multimedia Society
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• v.21 no.7
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• pp.772-778
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• 2018

We have previously proposed a preliminary cut-off indoor positioning scheme considering the reference point with the same signal similarity. This scheme estimates the position using the relative rank of the peak of received signal strength from the beacons around user. However, this scheme has a weak point with lower accuracy when there are more than one nearest reference points having the same signal similarity. In order to tackle this, we propose a weighted preliminary cut-off indoor positioning scheme. Firstly, if the above problem occurs, the similarity to the peak of signal strength is considered as well as the relative rank. Next, weights are assigned to the nearest reference points using the similarity to the peak of the received signal strength. Finally, the user's position is estimated by applying the weights. As a result, the weighted preliminary cut-off scheme improves the positioning accuracy by about 7.9% compared to the previous scheme.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.825-828
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• 1991

This paper addresses estimation of the relative shift vector from aerial image sequences. We perform similarity function tests and decide the most appropriate similarity function for the visual navigation system using aerial images. Finally, we propose the maximum variance reference line selection method for reducing the estimation error of the shift vector.

• Journal of the Ergonomics Society of Korea
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• v.21 no.3
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• pp.47-58
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• 2002

The similarity of shape of shape of interface between human cognitive map and real product is the important factor to determine the human performance. Nevertheless, the degree of similarity between these has not been defined quantitatively in recent studies. Therefore, in this study, the cognitive map and the mental workload were measured by SMM(Sketch Map Method) and RNASA-TLX(Revision of NASA-Task Load Index). And the numerical expression of the accuracy point was suggested for the quantitative calculation of relative positional similarity between cognitive map and real product. In the experiment, nine subjects were participated and two kinds of vehicles were used. Mental workload was mental workload was measured immediately after the road test. The result of analysis on the relationship between accuracy and mental workload shows that the negative correlation exists on each vehicle, and the lower score of mental workloads id measured on the vehicle that has the higher score of accuracy between two vehicles.