• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.463-470
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• 2010

We presented a very fast and robust method of text line segmentation based on the DCT blocks of color image without decompression and binary transformation processes. Using DC and another three primary AC coefficients from block DCT we created a gray-scale image having reduced size by 8x8. In order to detect and locate white strips between text lines we analyzed horizontal and vertical projection profiles of the image and we applied a direct markov model to recover the missing white strips by estimating hidden periodicity. We presented performance results. The results showed that our method was 40 - 100 times faster than traditional method.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.475-482
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• 2018

In this paper, we present a study on data augmentation methods for DNN (Deep Neural Network)-based audio tagging. In this system, an audio signal is converted into a mel-spectrogram and used as an input to the DNN for audio tagging. To cope with the problem associated with a small number of training data, we augment the training samples using time stretching, pitch shifting, dynamic range compression, and block mixing. In this paper, we derive optimal parameters and combinations for the augmentation methods through audio tagging simulations.

• Korean Journal of Optics and Photonics
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• v.24 no.5
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• pp.262-270
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• 2013

In this paper, we propose a modified optical CBC(Cipher Block Chaining) encryption method using optical XOR logic operations. The proposed method is optically implemented by using dual encoding and a free-space interconnected optical logic gate technique in order to process XOR operations in parallel. Also, we suggest a CBC encryption/decryption optical module which can be fabricated with simple optical architecture. The proposed method makes it possible to encrypt and decrypt vast two-dimensional data very quickly due to the fast optical parallel processing property, and provides more security strength than the conventional electronic CBC algorithm because of the longer security key with the two-dimensional array. Computer simulations show that the proposed method is very effective in CBC encryption processing and can be applied to even ECB(Electronic Code Book) mode and CFB(Cipher Feedback Block) mode.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1285-1303
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• 2019

In the current credit scoring system, the credit bureau gathers credit information from financial institutions and calculates a credit score based on it. However, because all sensitive credit information is stored in one central authority, there are possibilities of privacy violations and successful external attacks can breach large amounts of personal information. To handle this problem, we propose privacy-preserving credit scoring in which a user gathers credit information from financial institutions, calculates a credit score and proves that the score is calculated correctly using a zero-knowledge proof and a blockchain. In addition, we propose a zero-knowledge proof scheme that can efficiently prove committed inputs to check whether the inputs of a zero-knowledge proof are actually provided by financial institutions with a blockchain. This scheme provides perfect zero-knowledge unlike Agrawal et al.'s scheme, short CRSs and proofs, and fast proof and verification. We confirmed that the proposed credit scoring can be used in the real world by implementing it and experimenting with a credit score algorithm which is similar to that of the real world.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.89-94
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• 2016

This paper proposed a facial expression recognition approach using SIFT feature and SVM classifier. The SIFT was generally employed as feature descriptor at key-points in object recognition fields. However, this paper applied the SIFT descriptor as feature vector for facial expression recognition. In this paper, the facial feature was extracted by applying SIFT descriptor at each sub-block image without key-point detection procedure, and the facial expression recognition was performed using SVM classifier. The performance evaluation was carried out through comparison with binary pattern feature-based approaches such as LBP and LDP, and the CK facial expression database and the JAFFE facial expression database were used in the experiments. From the experimental results, the proposed method using SIFT descriptor showed performance improvements of 6.06% and 3.87% compared to previous approaches for CK database and JAFFE database, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.73-79
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• 2022

The Python language widely used in artificial intelligence (AI) projects is an interpreter language, and errors occur at runtime. In order to prevent project failure due to errors, it is necessary to handle exceptions in code that can cause exceptional situations in advance. In particular, in AI projects that require a lot of resources, exceptions that occur after long execution lead to a large waste of resources. However, since exception handling depends on the developer's experience, developers have difficulty determining the appropriate exception to catch. To solve this need, we propose an approach that recommends exceptions to catch to developers during development by learning the existing exception handling statements. The proposed method receives the source code of the try block as input and recommends exceptions to be handled in the except block. We evaluate our approach for a large project consisting of two frameworks. According to our evaluation results, the average AUPRC is 0.92 or higher when performing exception recommendation. The study results show that the proposed method can support the developer's exception handling with exception recommendation performance that outperforms the comparative models.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.7
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• pp.143-148
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• 2020

Recently, web services have been expanded to various areas with the evolution of cloud environment. Whenever a user accesses a web service, the user's log information is stored in the web server. This log information is used as data to analyze the user's web service tendencies and is also used as important data to track the user's system access when a security problem in the system occurs. Currently, most web servers manage user log information in a centralized manner. When user log information is managed in a centralized manner, it is simple in the side of operation, but has a disadvantage of being very vulnerable to external malicious attacks. In the case of centralized management, user log information stored in the web server can be arbitrarily manipulated by external attacks, and in severe cases, the manipulated information can be leaked. In this case, it not only decreases the trust of the web service, but also makes it difficult to trace the source and cause of the attack on the web server. In order to solve these problems, this paper proposes a new method of managing user log information in a cloud environment by applying blockchain technology as an alternative to the existing centralized log management method. The proposed method can manage log information safely from external attacks because user log information is distributed and stored in blockchain on a private network with cloud environment.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.766-777
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• 2021

This paper presents an improved scheme for detection of the physical broadcast channel (PBCH) in long-term evolution (LTE)-based fifth-generation (5G) multimedia broadcast and multicast services (MBMS) and 5G new radio (NR) for cellular broadcast. In the time domain, by combining the correlations between the received signal and primary synchronization signal (PSS) within all SS/PBCH blocks, the frame synchronization and the start position of the SS/PBCH blocks can be obtained. In this paper, to improve the detection performance of PBCH for 5G NR, a combining scheme of PBCH signals within a frame is proposed. In addition, the performance of the proposed detection scheme is evaluated and the performance is compared with the conventional scheme for PBCH detection of LTE-based 5G MBMS. The simulation results show that the detection performance of PBCH for 5G NR is improved by combining the PBCH signals and outperforms LTE-based 5G MBMS under the additive white Gaussian noise (AWGN), fixed, and mobile environments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.1
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• pp.101-111
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• 2004

Preneel, Govaerts, and Vandewalle considered the 64 basic ways to construct a collision resistant hash function from a block cipher. They regarded 12 of these 64 schemes as secure, though no proofs or formal claims were given. Black, Rogaway, and Shrimpton presented a more proof-centric look at the schemes from PGV. They proved that, in the black box model of block cipher, 12 of 64 compression functions are CRHFs and 20 of 64 extended hash functions are CRHFs. In this paper, we present 64 schemes of block-cipher-based universal one way hash functions using the main idea of PGV and analyze these schemes in the black box model. We will show that 30 of 64 compression function families UOWHF and 42 of 64 extended hash function families are UOWHF. One of the important results is that, in this black box model, we don't need the mask keys for the security of UOWHF in contrast with the results in general security model of UOWHF. Our results also support the assertion that building an efficient and secure UOWHF is easier than building an efficient and secure CRHF.

• The KIPS Transactions:PartA
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• v.14A no.5
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• pp.301-308
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• 2007

This paper proposes high performance coprocessor architecture for real time dense disparity computation based on a phase-based binocular stereo matching technique called local weighted phase-correlation(LWPC). The algorithm combines the robustness of wavelet based phase difference methods and the basic control strategy of phase correlation methods, which consists of 4 stages. For parallel and efficient hardware implementation, the proposed architecture employs SIMD(Single Instruction Multiple Data Stream) architecture for each functional stage and all stages work on pipelined mode. Such that the newly devised pipelined linear array processor is optimized for the case of row-column image processing eliminating the need for transposed memory while preserving generality and high throughput. The proposed architecture is implemented with Xilinx HDL tool and the required hardware resources are calculated in terms of look up tables, flip flops, slices, and the amount of memory. The result shows the possibility that the proposed architecture can be integrated into one chip while maintaining the processing speed at video rate.