• Bulletin of the Korean Chemical Society
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• v.31 no.11
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• pp.3159-3162
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• 2010

To realize highly selective recognition of oxalate in water, a new chemosensing ensemble that behaves highly selective colorimetric recognition of oxalate in water at pH 7.4 has been developed. The ensemble was constructed by a pyrrole containing mononuclear copper complex and chromeazurol S. The ensemble shows a highly selective recognition of oxalate through an obvious color change from blue to yellow upon the addition of oxalate, whereas, other dicarboxylates such as malonate, succinate, fumarate, maleate, glutarate, adipate, phthalate, isophthalate and terephthalate do not induce any noticeable color changes. The oxalate recognition process is not significantly affected by other coexisting dicarboxylate.

• Journal of Korean Port Research
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• v.12 no.2
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• pp.225-232
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• 1998

Todays, the efficient management of container has not been realized in container terminal, because of the excessive quantity of container transported and manual system. For the efficient and automated management of container in terminal, the automated container identifier recognition system in terminal is a significant problem. However, the identifier recognition rate is decreased owing to the difficulty of image preprocessing caused the refraction of container surface, the change of weather and the damaged identifier characters. Therefore, this paper proposes more accurate system for container identifier recognition as suggestion of LSPRD(Line-Scan Proper Region Detection) for stronger preprocessing against external noisy element and MBP(Momentum Back-Propagation) neural network to recognize the identifier.

• MALSORI
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• no.53
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• pp.119-127
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• 2005

Speech recognition is one of the user interface technologies in commanding and controlling any terminal such as a TV, PC, cellular phone etc. in a ubiquitous environment. In controlling a terminal, the mismatch between training and testing causes rapid performance degradation. That is, the mismatch decreases not only the performance of the recognition system but also the reliability of that. Therefore, the performance degradation due to the mismatch caused by the change of the environment should be necessarily compensated. Whenever the environment changes, environment adaptation is performed using the user's speech and the background noise of the changed environment and the performance is increased by employing the models appropriately transformed to the changed environment. So far, the research on the environment compensation has been done actively. However, the compensation method for the effect of distant-talking speech has not been developed yet. Thus, in this paper we apply MLLR-based environment adaptation to compensate for the effect of distant-talking speech and the performance is improved.

• Korean Journal of Human Ecology
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• v.24 no.4
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• pp.513-529
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• 2015

This study performed corroborative analysis by establishing hypothesis so as to corroboratively define the effect on brand attitude of psychological risk recognition in the case where consumers reading negative media news related to bakery recognize crisis communication on the basis of which point. According to corroborative analysis, the role of psychological crisis perception as parameter is confirmed in the causal relation between crisis communication recognition and brand attitude. Such result of study confirms that the positive change in crisis communication recognition reduces psychological risk perception to bakery products and such psychological risk perception eventually become factor which affects brand attitude over products. Such result of study suggests that when reading negative media news on bakery, the influence on consumer's evaluation of news on the basis of certain point and the influence on the formation of causal relation between psychological risk perception and brand attitude has scientific ground. In the aspect, the main result of this study is to find the clue that when comparing precedent study between crisis communication recognition and brand attitude, psychological risk perception is realized with brand attitude as media by verifying the parameter role of psychological risk perception.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.33-39
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• 2014

Automatic driving needs many functions such as the obstacle recognition, the lane recognition, and the lane change, etc. In this paper, we realized a system which automatically drove the three-kinds of vehicle driving course, to introduce and apply the concept of 'color recognition' that expands the scope of 'lane recognition' for vehicle driving. We made the reduced each course compared with RC(Radio Control) car size, and controlled the steering considering the position and the slope of the detection line and the speed. Because the RC car does not have the brake function, we consider the speed and the position of the detection line to stop the RC car.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.1137-1140
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• 2005

The work presented in this paper describes a Hidden Markov Model(HMM)-based framework for face recognition and face detection. The observation vectors used to characterize the statics of the HMM are obtained using the coefficients of the Karhuman-Loves Transform(KLT). The face recognition method presented in this paper reduces significantly the computational complexity of previous HMM-based face recognition systems, while slightly improving the recognition rate. In addition, the suggested method is more effective than the exiting ones in face extraction in terms of accuracy and others even under complex changes to the surroundings such as lighting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2289-2298
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• 1997

This paper proposes a new method to recognize map. In order to minimize the ripple effect of one recognition result affecting another, the structural information is represented with a hierarchical model. and the model is used in both the recognition and verification process. Furthermore, lines related to an entity are searched in a used in both the recognition and verification process. Furthermore, lines related to an entity are searched in a reduced search space by defining some relations between lines. When there is a mis-recognition after verificaiton, recognition process will be retired. In the process, the accurate result can obtained through the change of the parameter values used in the algorithm. As a result, the search space is reduced effectively, and even objects that embodies the broken lines and the crossed lines are recognized.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.748-755
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• 2010

In this paper we propose an improved face recognition method using Gabor tensor. Gabor transform is known to be able to represent characteristic feature in face and reduced environmental influence. It may contribute to improve face recognition ratio. We attempted to combine three-dimensional tensor from Gabor transform with MPCA(Multilinear PCA) and LDA. MPCA with tensor which use various features is more effective than traditional one or two dimensional PCA. It is known to be robust to the change of face expression or light. Proposed method is simulated by MATALB9 using ORL and Yale face database. Test result shows that recognition ratio is improved maximum 9~27% compared with exisisting face recognition method.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1258-1260
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• 2009

Generally, if we want to change the perspectivity of objects, we should change the position of object or camera, forward or backward. In this paper, recognition of the perspectivity of objects is proposed by using a new elemental image array which is made change the pinhole points horizontally.