• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3790-3803
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• 2018

Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

• Journal of the Semiconductor & Display Technology
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• v.8 no.2
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• pp.23-29
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• 2009

To increase the data capacity of one-dimensional symbology, 2D barcodes have been proposed a decade ago. In this paper, a new 2D barcode detection algorithm based on Local Binary Pattern is presented. To locate 2D barcode symbols, a texture analysis scheme based on the Local Binary Pattern is adopted, and a gray-scale projection with sub-pixel operation is utilized to separate the symbol precisely from the input image. Finally, the segmented symbol is normalized using the inverse perspective transformation for the decoding process. The proposed method ensures high performances under various lighting/printing conditions and strong perspective deformations. Experiments show that our method is very robust and efficient in detecting the symbol area for the various types of 2D barcodes.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.591-596
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• 1986

A binary continuous phase frequency shift keying (CPFSK), whose phase is a continuous function of time and instantaneous frequency is constant, is a bandwidth efficient constant envelope signalling scheme. A transmitting signal is formed by combined coding of a convolutional encoder and a binary CPFSK modulator. The signal is transmitted throuth additive white Gaussian noise(AWGN) channel. If the received signal is detected by a coherent maximum likelihood(ML) receiver, error probability can be expressed approximately in terms of minimum Euclidean distance. We propose rate 2/4 codes for the improvement of error performance without increating the data rate per bandwidth and the receiver complexity. Its minimum Euclidean distances are compared with those of rate \ulcornercodes as a function of modulation index and observation interval.

• Journal of Korea Multimedia Society
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• v.9 no.2
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• pp.215-225
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• 2006

Binary images, such as cartoon character images, text images and signature images, which consist of two values with black and white have more difficulties inserting imperceptible secret data than color images. Steganography using binary cover images is not easy to satisfy requirements for both the imperceptibility of stego images and a high embedding rate of secret data at the same time. In this paper, we propose a scheme that can get both the high quality of stego images and a high embedding rate by supplementing the advantages of previous research. In addition, the insertion of the proposed method changes only existing pixels of the imperceptible position and can embed the secret data of [$log_2(mn+1)-2$] bits in a block with size of $m{\times}n$.

• Korean Chemical Engineering Research
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• v.43 no.2
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• pp.259-265
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• 2005

Binary isothermal vapor-liquid equilibrium(VLE) data were measured for water+n-methylformamide(NMF), benzene+NMF and toluene+NMF systems by using headspace gas chromatography(HSGC) at 353.15K. Additionally, the ternary liquid-liquid Equilibrium(LLE) data were determined by measuring of tie-line for the systems of NMF+benzene+n-heptane and NMF+toluene+n-heptane at 298.15 K. The measured isothermal binary VLE data have no azeotropes and were correlated well with $g^E$ model equations of Margules, van Laar, Wilson, NRTL and UNIQUAC. The experimental ternary tie line data were also correlated well with NRTL and UNIQUAC models. Besides their accuracy was analyzed by Hirata-Fujita and Maior-Swenson equations.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.1
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• pp.13-21
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• 2010

The prediction of contract cancellation of customers is essential in insurance companies but it is a difficult problem because the customer database is large and the target or cancelled customers are a small proportion of the database. This paper proposes a new data mining approach to the binary classification by handling a large-scale unbalanced data. Over-sampling, clustering, regularized logistic regression and boosting are also incorporated in the proposed approach. The proposed approach was applied to a real data set in the area of insurance and the results were compared with some other classification techniques.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1485-1494
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• 2015

Traditionally, data hiding has been done mainly in such a way that insert the data into the large-capacity multimedia files. However, the document files of the previous versions of Microsoft Office 2003 have been used as cover files as their structure are so similar to a File System that it is easy to hide data in them. If you open a compound-document file which has a secret message hidden in it with MS Office application, it is hard for users who don't know whether a secret message is hidden in the compound-document file to detect the secret message. This paper presents an analysis of Compound-File Binary Format features exploited in order to hide data and algorithms to detect the data hidden with these exploits. Studying methods used to hide data in unused area, unallocated area, reserved area and inserted streams led us to develop an algorithm to aid in the detection and examination of hidden data.

• Proceedings of the KIPE Conference
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• 2004.07a
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• pp.155-158
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• 2004

This paper describes a lane following control of vision based mobile robot that follows guidline. Summation of binarization conversion and image data of vertical axis was used in image processing. As an extraction of specific parameters of lane image, the raw image was converted to the binary data, and the binary data was summerized to the specific data vertically. The specific parameters were made to the inputs of neural network. Summation of image data was used for input of the net, and optimized value of turn angles of learned mobile robot was output. By using neural network algorithm, possibility of mobile robot moving to the target point and following the guidlines quickly and effectively was proved.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.342-357
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• 2014

The present study analyzes the relationship between research and development (R&D) inputs and performance of a national technology innovation R&D program using successive binary Logistic regression models based on a typical R&D logic model. In particular, this study focuses on to answer the following three main questions; (1) "To what extent, do the R&D inputs have an effect on the performance creation?"; (2) "Is an obvious relationship verified between the immediate predecessor and its successor performance?"; and (3) "Is there a difference in the performance creation between R&D government subsidy recipient types and between R&D collaboration types?" Methodologically, binary Logistic regression models are established successively considering the "Success-Failure" binary data characteristic regarding the performance creation. An empirical analysis is presented analyzing the sample n = 2,178 R&D projects completed. This study's major findings are as follows. First, the R&D inputs have a statistically significant relationship only with the short-term, technical output, "Patent Registration." Second, strong dependencies are identified between the immediate predecessor and its successor performance. Third, the success probability of the performance creation is statistically significantly different between the R&D types aforementioned. Specifically, compared with "Large Company", "Small and Medium-Sized Enterprise (SMS)" shows a greater success probability of "Sales" and "New Employment." Meanwhile, "R&D Collaboration" achieves a larger success probability of "Patent Registration" and "Sales."