• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.2
• /
• pp.51-58
• /
• 2022

Blockchain technology is a system in which data from users participating in blockchain networks is distributed and stored. Bitcoin and Ethereum are attracting global attention, and the utilization of blockchain is expected to be endless. However, the need for blockchain data privacy protection is emerging in various financial, medical, and real estate sectors that process personal information due to the transparency of disclosing all data in the blockchain to network participants. Although studies using smart contracts, homomorphic encryption, and cryptographic key methods have been mainly conducted to protect existing blockchain data privacy, this paper proposes data privacy using matrix character relocation techniques differentiated from existing papers. The approach proposed in this paper consists largely of two methods: how to relocate the original data to matrix characters, how to return the deployed data to the original. Through qualitative experiments, we evaluate the safety of the approach proposed in this paper, and demonstrate that matrix character relocation will be sufficiently applicable in private blockchain environments by measuring the time it takes to revert applied data to original data.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.17 no.4
• /
• pp.53-62
• /
• 2021

Deep learning models such as convolutional neural networks and recurrent neual networks process a huge amounts of data, so they require a lot of storage and consume a lot of time and power due to memory access. Recently, research is being conducted to reduce memory usage and access by compressing data using the feature that many of deep learning data are highly sparse and localized. In this paper, we propose a compression-decompression method of storing only the non-zero data and the location information of the non-zero data excluding zero data. In order to make the location information of non-zero data, the matrix data is divided into sections uniformly. And whether there is non-zero data in the corresponding section is indicated. In this case, section division is not executed only once, but repeatedly executed, and location information is stored in each step. Therefore, it can be properly compressed according to the ratio and distribution of zero data. In addition, we propose a hardware structure that enables compression and decompression without complex operations. It was designed and verified with Verilog, and it was confirmed that it can be used in hardware deep learning accelerators.

• Nuclear Engineering and Technology
• /
• v.45 no.7
• /
• pp.827-838
• /
• 2013

Interaction layer growth between U-Mo alloy fuel particles and Al in a dispersion fuel is a concern due to the volume expansion and other unfavorable irradiation behavior of the interaction product. To reduce interaction layer (IL) growth, a small amount of Si is added to the Al. As a result, IL growth is affected by the Si content in the Al matrix. In order to predict IL growth during fabrication and irradiation, empirical models were developed. For IL growth prediction during fabrication and any follow-on heating process before irradiation, out-of-pile heating test data were used to develop kinetic correlations. Two out-of-pile correlations, one for the pure Al matrix and the other for the Al matrix with Si addition, respectively, were developed, which are Arrhenius equations that include temperature and time. For IL growth predictions during irradiation, the out-of-pile correlations were modified to include a fission-rate term to consider fission enhanced diffusion, and multiplication factors to incorporate the Si addition effect and the effect of the Mo content. The in-pile correlation is applicable for a pure Al matrix and an Al matrix with the Si content up to 8 wt%, for fuel temperatures up to $200^{\circ}C$, and for Mo content in the range of 6 - 10wt%. In order to cover these ranges, in-pile data were included in modeling from various tests, such as the US RERTR-4, -5, -6, -7 and -9 tests and Korea's KOMO-4 test, that were designed to systematically examine the effects of the fission rate, temperature, Si content in Al matrix, and Mo content in U-Mo particles. A model converting the IL thickness to the IL volume fraction in the meat was also developed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.12
• /
• pp.5860-5876
• /
• 2018

A single snapshot data can only provide limited amount of information so that the rank of covariance matrix is not full, which is not adopted to complete the parameter estimation directly using the traditional super-resolution method. Aiming at solving the problem, a joint time delay and angle estimation using matrix pencil method based on information reconstruction vector for orthogonal frequency division multiplexing (OFDM) signal is proposed. Firstly, according to the channel frequency response vector of each array element, the algorithm reconstructs the vector data with delay and angle parameter information from both frequency and space dimensions. Then the enhanced data matrix for the extended array element is constructed, and the parameter vector of time delay and angle is estimated by the two-dimensional matrix pencil (2D MP) algorithm. Finally, the joint estimation of two-dimensional parameters is accomplished by the parameter pairing. The algorithm does not need a pseudo-spectral peak search, and the location of the target can be determined only by a single receiver, which can reduce the overhead of the positioning system. The theoretical analysis and simulation results show that the estimation accuracy of the proposed method in a single snapshot and low signal-to-noise ratio environment is much higher than that of Root Multiple Signal Classification algorithm (Root-MUSIC), and this method also achieves the higher estimation performance and efficiency with lower complexity cost compared to the one-dimensional matrix pencil algorithm.

• The Pure and Applied Mathematics
• /
• v.11 no.1
• /
• pp.1-14
• /
• 2004

Due to the development of computer network and mobile communications, the security in image data and other related source are very important as in saving or transferring the commercial documents, medical data, and every private picture. Nonetheless, the conventional encryption algorithms are usually focusing on the word message. These methods are too complicated or complex in the respect of image data because they have much more amounts of information to represent. In this sense, we proposed an efficient secret symmetric stream type encryption algorithm which is based on Boolean matrix operation and the characteristic of image data.

• Journal of Astronomy and Space Sciences
• /
• v.29 no.3
• /
• pp.287-293
• /
• 2012

Satellite operating agencies are constantly monitoring conjunctions between satellites and space objects. Two line element (TLE) data, published by the Joint Space Operations Center of the United States Strategic Command, are available as raw data for a preliminary analysis of initial conjunction with a space object without any orbital information. However, there exist several sorts of uncertainties in the TLE data. In this paper, we suggest and analyze a method for estimating the uncertainties in the TLE data through mean, standard deviation of state vector residuals and covariance matrix. Also the estimation results are compared with actual results of orbit determination to validate the estimation method. Characteristics of the state vector residuals depending on the orbital elements are examined by applying the analysis to several satellites in various orbits. Main source of difference between the covariance matrices are also analyzed by comparing the matrices. Particularly, for the Korea Multi-Purpose Satellite-2, we examine the characteristics of the residual variation of state vector and covariance matrix depending on the orbital elements. It is confirmed that a realistic consideration on the space situation of space objects is possible using information from the analysis of mean, standard deviation of the state vector residuals of TLE and covariance matrix.

• Journal of Power Electronics
• /
• v.10 no.6
• /
• pp.611-620
• /
• 2010

This paper presents a new architecture, called "Power Electronics Remote Wiring and Measurement Laboratory (PermLAB)", that translates a common gateway interface (CGI) string from a remote web user to a web server connected to a 3-dimension switching matrix board, can be used to switch on and off, and to control a cluster of instruments and components. PermLAB addresses real-time connection, switching, and data acquisition over the Internet instead of using simulated data. A software procedure uses a signature system to identify each instrument and component in a complex system. The Web-server application is developed in HTML, JavaScript and Java, and in C language for the CGI interface, which resides in a controller portion of LabVIEW. The LabVIEW software fully integrates the Web sever, LabVIEW data acquisition boards and controllers, and the 3-dimensional switching matrix board. The paper will analyze a half-wave rectifier (AC - DC converter) circuit connected over the Internet using the PermLAB. PermLAB allows students to obtain real data by real-time wiring of real circuits in the laboratory using a "virtual breadboard" on the Web. The software for the Web-based 3-dimensional system is flexible, portable, can be integrated into many laboratory applications or expanded, and easily accessible worldwide.

• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.453-456
• /
• 1992

A Data-based Covariance Matrix(DCM) is introduced in the Eigenvector(EV) method, among subspace methods of estimating multiple sinusoidal frequencies from finite white noisy measurements. It is shown that the EV with the DCM can obtain the true. frequencies from finite noiseless data Some asymptotic results and further improvement on the DCM are also presented mathematically. Monte-carlo simulations are statistically conducted from the view-points of means and standard deviations in the EV's of DCM and Conventional Covariance Matrix(CCM). Simulations show a great promise for using the DCM, particularly for the cases of short data records, closely spaced frequencies and high signal-to-noise ratios.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.999-1007
• /
• 2013

This research shows several types of moving pictures from the data: 1) the word cloud of Korean texts, 2) the heat map of n ${\times}$ p matrices, 3) the moving image of p ${\times}$ p scatterplot matrix, 4) the local projective display of k clusters (Huh and Lee, 2012). Moving pictures may reveal the hidden information and beauty of the datasets and ignite the curiosity of information consumers. Video files are attached.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.34 no.3
• /
• pp.3-10
• /
• 2018

It's not practical to collect all information at the entire degrees of freedom of finite element model. The incomplete measurements should be expanded for subsequent analysis and damage detection. This work presents the analytical methods to expand the incomplete static or dynamic response data. Using the expanded data, introducing the concept of residual force, and minimizing the performance index expressed as the stiffness matrix and its difference before and after damage, the variation in stiffness matrix is derived. Based on the difference in the stiffness matrix, the damage detection method of structures is also provided. The validity of the proposed methods is illustrated in a numerical application, the numerical results are analyzed for applications, and the applicability of both methods is investigated.