• Convergence Security Journal
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• v.18 no.1
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• pp.19-31
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• 2018

As the ICT technologies advance, the unprecedently large amount of digital data is created, transferred, stored, and utilized in every industry. With the data scale extension and the applying technologies advancement, the new services emerging from the use of large scale data make our living more convenient and useful. But the cybercrimes such as data forgery and/or change of data generation time are also increasing. For the data security against the cybercrimes, the technology for data integrity and the time verification are necessary. Today, public key based signature technology is the most commonly used. But a lot of costly system resources and the additional infra to manage the certificates and keys for using it make it impractical to use in the large-scale data environment. In this research, a new and far less system resources consuming signature technology for large scale data, based on the Hash Function and Merkle tree, is introduced. An improved method for processing the distributed hash trees is also suggested to mitigate the disruptions by server failures. The prototype system was implemented, and its performance was evaluated. The results show that the technology can be effectively used in a variety of areas like cloud computing, IoT, big data, fin-tech, etc., which produce a large-scale data.

• Journal of Life Science
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• v.31 no.2
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• pp.149-157
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• 2021

Prunus mume Sieb. et Zucc is widely distributed in East Asia (Korea, Japan, and China), and its fruit is often used as a medication and food material. However, because most previous studies have only investigated the state of Prunus mume fruit extract, studies on the various ways of processing this extract are still needed to increase its utilization. In this study, we evaluated the physicochemical properties and physiological activities of spray-dried Prunus mume vinegar powder (SPP). The sugar content, pH, total acidity, and moisture content of the SPP were 8.90 °Brix, 3.19, 1.05%, and 3.07%, respectively. The SPP exhibited significantly high antioxidant activity in terms of DPPH radical scavenging activity (65.55%), reducing power (1.48), and hydrogen peroxide scavenging activity (48.07%). In addition, the SPP remarkably decreased the cell viability of human breast MDA-MB-231 and human skin cancer SK-MEL-28 in a dose-dependent manner. The morphological results of the treatment of MDA-MB-231 cells with SPP were distorted, shrunken cell masses. Furthermore, apoptotic bodies and nuclear condensation formed in the SPP-treated MDA-MB-231 cells. The total polyphenol and flavonoid contents of the SPP were 59.58 ㎍/g (gallic acid equivalent) and 57.56 ㎍/g (quercetin equivalent). The results of this study indicate that SPP, which has antioxidant activity and anticancer effects, can be useful in the development of natural medicines and functional food ingredients.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.6
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• pp.823-833
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• 2020

The Manila clam Ruditapes philippinarum, is an important marine bivalve that is widely distributed along the west and north coasts of South Korea. It has been used in a variety of Korean foods owing to its superior umami taste. In the present study, we developed a flavoring with an excellent sensory preference from Manila clam using extrusion cooking processing. Optimization of extrusion cooking conditions was performed using response surface methodology (RSM). Barrel temperature (X1, 140-160℃) and screw speed (X2, 400-560 rpm) of the extruder were chosen as independent variables. The dependent variable was overall acceptance (Y, points). The estimated optimal conditions were as follows: overall acceptance (Y): X1=140℃ and X2=560 rpm. The indicated value of the dependent variable overall acceptance (Y) under the optimal conditions was 8.94 points, which was similar to the experimental value (8.82 points). Overall acceptance of the Manila clam flavoring was related to its umami and Manila clam tastes. The electronic nose and tongue results successfully segregated different clusters of the samples between the lowest and highest sensory scores. The sample with the highest sensory score had higher sourness, umami, and sweetness intensities, and the lowest sensory scored sample showed more off-flavor compounds.

• Smart Media Journal
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• v.10 no.1
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• pp.88-98
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• 2021

As blockchain technology has emerged as a security issue for IoT, technology which integrates block chain into IoT is being studied. In this paper is a research concerning token-based IoT service access control technology for data sharing, which propose a possessor focused data sharing technic by using the permissioned blockchain. To share IoT health data, a Hyperledger Fabric Network consisting of three organizations was designed to provide a way to share data by applying different access control policies centered on device owners for different services. In the proposed system, the device owner issues access control tokens with different security levels applied to the participants in the organization, and the token issue information is shared through the distributed ledger of the HFN. In IoT, it is possible to lightweight the access control processing of IoT devices by granting tokens to service requesters who request access to data. Furthmore, by sharing token issuance information among network participants using HFN, the integrity of the token is guaranteed and all network participants can trust the token. The device owners can trust that their data is being used within their authorized rights, and control the collection and use of data.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.463-473
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• 2021

Recently, United States Geological Survey (USGS) distributed Landsat 8 Collection 2 Level 2 Science Product (L2SP). This paper aims to derive land surface temperature from L2SP and to validate it. Validation is made by comparing the land surface temperature with the one calculated from Landsat 8 Collection 1 Level 1 Terrain Precision (L1TP) and the one from Automated Synoptic Observing System (ASOS). L2SP is calculated from Landsat 8 Collection 2 Level 1 data and it provides land surface temperature to users without processing surface reflectance data. Landsat 8 data from 2018 to 2020 is collected and ground sensor data from eight sites of ASOS are used to evaluate L2SP land surface temperature data. To compare ground sensor data with remotely sensed data, 3×3 grid area data near ASOS station is used. As a result of analysis with ASOS data, L2SP and L1TP land surface temperature shows Pearson correlation coefficient of 0.971 and 0.964, respectively. RMSE (Root Mean Square Error) of two results with ASOS data is 4.029℃, 5.247℃ respectively. This result suggests that L2SP data is more adequate to acquire land surface temperature than L1TP. If seasonal difference and geometric features such as slope are considered, the result would improve.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.377-388
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• 2022

Recently, several studies have utilized machine learning to efficiently and accurately analyze seismic data that are exponentially increasing. In this study, we expand earthquake information such as occurrence time, hypocentral location, and magnitude to produce a dataset for applying to machine learning, reducing the dimension of the expended data into dominant features through principal component analysis. The dimensional extended data comprises statistics of the earthquake information from the Global Centroid Moment Tensor catalog containing 36,699 seismic events. We perform data preprocessing using standard and max-min scaling and extract dominant features with principal components analysis from the scaled dataset. The scaling methods significantly reduced the deviation of feature values caused by different units. Among them, the standard scaling method transforms the median of each feature with a smaller deviation than other scaling methods. The six principal components extracted from the non-scaled dataset explain 99% of the original data. The sixteen principal components from the datasets, which are applied with standardization or max-min scaling, reconstruct 98% of the original datasets. These results indicate that more principal components are needed to preserve original data information with even distributed feature values. We propose a data processing method for efficient and accurate machine learning model to analyze the relationship between seismic data and seismic behavior.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.11-16
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• 2022

As an interest in the development of artificial intelligence(AI) technology in the water supply sector increases, we have developed an AI algorithm that can predict improvement decision-making ratings through repetitive learning using the data of pipe condition evaluation results, and present the most reliable prediction model through a verification process. We have developed the algorithm that can predict pipe ratings by pre-processing 12 indirect evaluation items based on the 2020 Han River Basin's basic plan and applying the AI algorithm to update weighting factors through backpropagation. This method ensured that the concordance rate between the direct evaluation result value and the calculated result value through repetitive learning and verification was more than 90%. As a result of the algorithm accuracy verification process, it was confirmed that all water pipe type data were evenly distributed, and the more learning data, the higher prediction accuracy. If data from all across the country is collected, the reliability of the prediction technique for pipe ratings using AI algorithm will be improved, and therefore, it is expected that the AI algorithm will play a role in supporting decision-making in the objective evaluation of the condition of aging pipes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1213-1221
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• 2022

In Electronic Warfare, the need to develop a jamming system that protects our location information from enemy radar is constantly increasing. The jamming system normally uses wide-band DRFM(Digital Radio Frequency Memory) that processes the entire bandwidth at once. However, it is difficult to jam if there is a CW(Continuous Wave) interference signal in the band. Recently, instead of wide-band signal processing, a structure using a filter bank that divides the entire band into several sub-bands and processes each sub-band independently has been proposed. Although it is possible to handle interference signal through the filter bank structure, spurious signal occurs when the signal is received at a boundary frequency between sub-bands. Spurious signal makes a output power of jamming signal distributed, resulting in lower JSR(Jamming to Signal Ratio) and less jamming effect. This paper proposes an over-sampled channelized DRFM structure that enables interference response and prevents spurious signal for sub-band boundary frequency input.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1404-1415
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• 2022

The 2-dimensional arrangement method of nodes has been used in most of RF (Radio Frequency) based communication network simulations. However, this method is not useful for the an none-obstacle 3-dimensional space networks in which the propagation delay speed in communication is very slow and, moreover, the values of performance factors such as the communication speed and the error rate change on the depth of node. Such a typical example is an underwater communication network. The 2-dimensional arrangement method is also not useful for the RF based network like some WSNs (Wireless Sensor Networks), IBSs (Intelligent Building Systems), or smart homes, in which the distance between nodes is short or some of nodes can be arranged overlapping with their different heights in similar planar location. In such cases, the 2-dimensional network simulation results are highly inaccurate and unbelievable so that they lead to user's erroneous predictions and judgments. For these reasons, in this paper, we propose a method to place uniformly and randomly communication nodes in 3-dimensional network space, making the wireless link with neighbor node possible. In this method, based on the communication rage of the node, blocks are generated to construct the 3-dimensional network and a node per one block is generated and placed within a block area. In this paper, we also introduce an algorithm based on this method and we show the performance results and evaluations on the average time in a node generation and arrangement, and the arrangement time and scatter-plotted visualization time of all nodes according to the number of them. In addition, comparison with previous studies is conducted. As a result of evaluating the performance of the algorithm, it was found that the processing time of the algorithm was proportional to the number of nodes to be created, and the average generation time of one node was between 0.238 and 0.28 us. ultimately, There is no problem even if a simulation network with a large number of nodes is created, so it can be sufficiently introduced at the time of simulation.

• Analytical Science and Technology
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• v.35 no.5
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• pp.205-211
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• 2022

Deuterium (D) is an isotope with one more neutron number than hydrogen (H). Heavy elements rarely change their chemical properties with little effect even if the number of neutrons increases, but low-mass elements change their vibration energy, diffusion rate, and reaction rate because the effect cannot be ignored, which is called an isotope effect. Recently, in the semiconductor and display industries, there is a trend to replace hydrogen gas (H₂) with deuterium gas (D₂) in order to improve process stability and product quality by using the isotope effect. In addition, as the demand for D₂ in industries increases, domestic gas producers are making efforts to produce and supply D₂ on their own. In the case of high purity D₂, most of them are produced by electrolysis of heavy water (D₂O), and among D₂, hydrogen deuteride (HD) molecules are present as isotope impurities. Therefore, in order to maximize the isotope effect of hydrogen in the electronic industry, HD, which is an isotope impurity of D₂ used in the process, should be small amount. To this end, purity analysis of D₂ for industrial processing is essential. In this study, HD quantitative analysis of D₂ for high purity D₂ purity analysis was established and hydrogen isotope RM (Reference material) was developed. Since hydrogen isotopes are difficult to analyze with general gas analysis instrument, they were analyzed using a high-precision mass spectrometer (Gas/MS, Finnigan MAT271). High purity HD gas was injected into Gas/MS, sensitivity was determined by a signal according to pressure, and HD concentrations in two bottles of D₂ were quantified using the corresponding sensitivity. The amount fraction of HD in each D₂ was (4518 ± 275) μmol/mol, (2282 ± 144) μmol/mol. D₂, which quantifies HD amount using the developed quantitative analysis method, will be manufactured with hydrogen isotope RM and distributed for quality management and maintenance of electronic industries and gas producers in the future.