• Biotechnology and Bioprocess Engineering:BBE
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• v.6 no.6
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• pp.386-394
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• 2001

Carboxylic acids are examples of compounds with wide industrial applications and high potential, This article presents the principles of reactive extraction along with the character-istics of tertiary amine extractants, while is given on considering the effect of the amine class and chain length, As such a brief overview the current research on reactive extraction, including the recovery of citric acid, selective amine-based extraction , and extractive fermentation is given. When discussing extractive fermentation strategies for reducing solvent toxicity are also suggested based on specific examples. Finally, solvent regeneration and stripping of extracted acid explained.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.444-450
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• 1999

This paper deals with several methods: the clustering method that uses k-means algorithm to abstract the area of characters on the image document and the distance function that suits for the HIS coordinate system to cluster the image. For the prepossessing step to recognize this, or the method of characters segmentate, the algorithm to abstract a discrete character is also proposed, using the linking picture element. This algorithm provides the feature that separates any character such as the touching or overlapped character. The methods of projecting and tracking the edge have so far been used to segment them. However, with the new method proposed here, the picture element extracts a discrete character with only one-time projection after abstracting the character string. it is possible to pull out it. dividing the area into the character and the rest (non-character). This has great significance in terms of processing color documents, not the simple binary image, and already received verification that it is more advanced than the previous document processing system.

• Journal of Navigation and Port Research
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• v.28 no.8
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• pp.727-733
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• 2004

In the car license plate recognition system, it is very important to extract the part of the license plate from the car image. In this paper, I use ACL algorithm to extract the license plate image from car image. The ACL algorithm is used to color and luminance information, either. Therefore in this paper, suggested algorithm is called ACL algorithm The ACL algorithm uses color, luminance information and the rate of license plate information Each of these information are used to exact area of license plate. The result of experiment to extract the car license plate with ACL algorithm is 97% extraction rate. The result of experiment with ACL algorithm for the character region, character recognition is 92% extraction rate.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.53-56
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• 2002

In this paper, we propose the method of iris lacuna extraction using watershed transform. Lacuna is salient feature of iris. It has three dimensional structure formed by leak of pigmentation and loss of fiber tissues. Lacuna can be used for iris recognition system, and generally used in health diagnosis and character analysis with its shape and position. The main idea of the proposed method is applying the watershed transform to radial gray scale profile of iris image. The result shows that the lacuna can be extracted automatically from eye image.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.831-842
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• 1999

We propose a wavelet decomposition feature extraction method for the hand-written character recognition. Comparing the recognition rates of which methods with original image features and with selected features by the wavelet decomposition we study the characteristics of the proposed method. LDA(Linear Discriminant Analysis) QDA(Quadratic Discriminant Analysis) RDA(Regularized Discriminant Analysis) and NN(Neural network) are used for the calculation of recognition rates. 6000 hand-written numerals from CENPARMI at Concordia University are used for the experiment. We found that the set of significantly selected wavelet decomposed features generates higher recognition rate than the original image features.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1183-1186
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• 2004

In the steel production line, the molten metal of a furnace is transformed into billet and then moves to the heating furnace of the hot rolling mill. This paper describes about the realtime billet characters recognition system in the steel production line. Normally, the billets are mixed at yard so that their identifications are very difficult and very important processing. The character recognition algorithm used in this paper is base on the subspace method by K-L transformation. With this method, we need no special feature extraction steps, which are usually error prone. So the gray character images are directly used as input vectors of the classifier. To train the classifier, we have extracted eigen vectors of each character used in the billet numbers, which consists of 10 arabia numbers and 26 alphabet aharacters, which are gathered from billet images of the production line. We have developed billet characters recognition system using this algorithm and tested this system in the steel production line during the 8-days. The recognition rate of our system in the field test has turned out to be 94.1% (98.6% if the corrupted characters are excluded). In the results, we confirmed that our recognition system has a good performance in the poor environments and ill-conditioned marking system like as steel production plant.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1819-1820
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• 2008

Steel slabs are marked with slab management numbers (SMNs). To increase efficiency, automated identification of SMNs from digital images is desirable. Automatic extraction of SMNs is a prerequisite for automatic character segmentation and recognition. The images include complex background, and the position of the text region of the slabs is variable. This paper describes an pre-processing algorithm for detection of slab information using robust feature points extraction. Using SIFT(Scale Invariant Feature Transform) algorithm, we can reduce the search region for extraction of SMNs from the slab image.

• International Journal of Advanced Culture Technology
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• v.6 no.2
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• pp.123-128
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• 2018

Learning data is composed of 100 characters with 10 different fonts, and test data is composed of 10 characters with a new font that is not used for the learning data. In order to consider the variety of learning data with several different fonts, 10 learning masks are constructed by accumulating pixel values of same characters with 10 different fonts. This process eliminates minute difference of characters with different fonts. After finding maximum values of learning masks, test data is expanded by multiplying these maximum values to the test data. The algorithm calculates sum of differences of two corresponding pixel values of the expanded test data and the learning masks. The learning mask with the smallest value among these 10 calculated sums is selected as the result of the recognition process for the test data. The proposed algorithm can recognize various types of fonts, and the learning data can be modified easily by adding a new font. Also, the recognition process is easy to understand, and the algorithm makes satisfactory results for character recognition.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.54-57
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• 2003

In this paper, we propose an algorithm extracting character regions from scenery images. This algorithm works under a severe constraint: each pixel of a result image must be derived from only information of their neighbor pixels. This constraint is very important for a low cost device like a mobile camera. The proposed algorithm is represented by the local and parallel image processing. It has been tested for 100 scenery images. A result shows that the proposed algorithm can extract character regions at a rate of more than 90％. The result was obtained without learning any template images. the algorithm is very useful.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.6
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• pp.455-466
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• 1990

Enalish character recognition with the neural networl algorithm has been performed. Character recognition technition techniques which are processed by software, have the limit of the recognition speed. To overcome this limit, we realize this system to hardware by using the neural network algorithm. We have designed preprocessing chip using the neural nework model, that is single layer perceptorn, in the noise elimination, smoothing, thinning and feature point extraction. These chips are implemented as a CMOS double metal 2um design rule.