• KIPS Transactions on Computer and Communication Systems
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• v.11 no.8
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• pp.249-258
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• 2022

Since the advent of Bitcoin, various virtual assets have been actively traded through virtual asset services of virtual asset exchanges. Recently, security accidents have frequently occurred in virtual asset exchanges, so the government is obligated to obtain information security management system (ISMS) certification to strengthen information protection of virtual asset exchanges, and 56 additional specialized items have been established. In this paper, we compared the domain importance of ISMS and CryptoCurrency Security Standard (CCSS) which is a set of requirements for all information systems that make use of cryptocurrencies, and analyzed the results after mapping them to gain insight into the characteristics of each certification system. Improvements for 4 items of High Level were derived by classifying the priorities for improvement items into 3 stages: High, Medium, and Low. These results can provide priority for virtual asset and information system security, support method and systematic decision-making on improvement of certified items, and contribute to vitalization of virtual asset transactions by enhancing the reliability and safety of virtual asset services.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.39 no.4
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• pp.348-352
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• 1994

Soybean seed consists of two major storage protein, the 7S and 11S globulins. For improving the quality of soybean seed protein, an increase of 11S/7S ratio would be a desirable objective because the 11S globulin contains much more the sulfur-containing amino acids than the 7S globulin. In this study, some soybean varieties were used to investigate the analyzing method for 7S and 11S globulins. 7S and 11S globulins couble be fractionated by their different solubilities in tris buffers. Adjusting the pH and tris concentration were major factors affecting the precipitation of the two globulins. And it was possible to screen the soybean genotypes having aberrant subunit compositions of the two globulins by an sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) of total soybean proteins. The ratio of 11S to 7S globulin ranged from 1.29 to 1.38. This paper also dealed with the contribution of protein components in soybean seeds to the physical properties of soycurd. It indicated that the soycurd from crude 11S was remarkably harder than that from crude 7S, and springiness and cohesiveness were slightly higher in soycurd having higher proportion of 11S. So, it may concluded that proportion of protein components in soybean seed can be important factor which controls the suitability for soycurd or other foods.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1246-1246
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• 2001

The quality of meat is highly variable in many properties. This variability originates from both animal production and meat processing. At the pre-slaughter stage, animal factors such as breed, sex, age contribute to this variability. Environmental factors include feeding, rearing, transport and conditions just before slaughter (Hildrum et al., 1995). Meat can be presented in a variety of forms, each offering different opportunities for adulteration and contamination. This has imposed great pressure on the food manufacturing industry to guarantee the safety of meat. Tissue and muscle speciation of flesh foods, as well as speciation of animal derived by-products fed to all classes of domestic animals, are now perhaps the most important uncertainty which the food industry must resolve to allay consumer concern. Recently, there is a demand for rapid and low cost methods of direct quality measurements in both food and food ingredients (including high performance liquid chromatography (HPLC), thin layer chromatography (TLC), enzymatic and inmunological tests (e.g. ELISA test) and physical tests) to establish their authenticity and hence guarantee the quality of products manufactured for consumers (Holland et al., 1998). The use of Near Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise and non-destructive analysis of a wide range of organic materials has been comprehensively documented (Osborne et at., 1993). Most of the established methods have involved the development of NIRS calibrations for the quantitative prediction of composition in meat (Ben-Gera and Norris, 1968; Lanza, 1983; Clark and Short, 1994). This was a rational strategy to pursue during the initial stages of its application, given the type of equipment available, the state of development of the emerging discipline of chemometrics and the overwhelming commercial interest in solving such problems (Downey, 1994). One of the advantages of NIRS technology is not only to assess chemical structures through the analysis of the molecular bonds in the near infrared spectrum, but also to build an optical model characteristic of the sample which behaves like the “finger print” of the sample. This opens the possibility of using spectra to determine complex attributes of organic structures, which are related to molecular chromophores, organoleptic scores and sensory characteristics (Hildrum et al., 1994, 1995; Park et al., 1998). In addition, the application of statistical packages like principal component or discriminant analysis provides the possibility to understand the optical properties of the sample and make a classification without the chemical information. The objectives of this present work were: (1) to examine two methods of sample presentation to the instrument (intact and minced) and (2) to explore the use of principal component analysis (PCA) and Soft Independent Modelling of class Analogy (SIMCA) to classify muscles by quality attributes. Seventy-eight (n: 78) beef muscles (m. longissimus dorsi) from Hereford breed of cattle were used. The samples were scanned in a NIRS monochromator instrument (NIR Systems 6500, Silver Spring, MD, USA) in reflectance mode (log 1/R). Both intact and minced presentation to the instrument were explored. Qualitative analysis of optical information through PCA and SIMCA analysis showed differences in muscles resulting from two different feeding systems.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.71-76
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• 2023

Recently, in order to store and manage big data, research and development of a high-performance storage system capable of stably accessing large data have been actively conducted. In particular, storage systems in data centers and enterprise environments use large amounts of SSD (solid state disk) to manage large amounts of data. In general, SSD uses FTL(flash transfer layer) to hide the characteristics of NAND flash memory, which is a medium, and to efficiently manage data. However, FTL's algorithm has a limitation in using DRAM more to manage the location information of NAND where data is stored as the capacity of SSD increases. Therefore, this paper introduces FTL policies that apply virtual memory to reduce DRAM resources used in FTL. The virtual memory-based FTL policy proposed in this paper manages the map data by using LRU (least recently used) policy to load the mapping information of the recently used data into the DRAM space and store the previously used information in NAND. Finally, through experiments, performance and resource usage consumed during data write processing of virtual memory-based FTL and general FTL are measured and analyzed.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.1-8
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• 2023

Modeling of artificial targets in Synthetic Aperture radar (SAR) mainly simulates radar signals reflected from the faces and edges of the 3D Computer Aided Design (CAD) model with a ray-tracing method, and modeling of the clutter on the Earth's surface uses a method of distinguishing types with similar distribution characteristics through statistical analysis of the SAR image itself. In this paper, man-made targets on the surface and background clutter on the terrain are integrated and made into a three-dimensional (3D) point cloud scatterer model, and SAR image were created through computational signal processing. The results of the SAR Stripmap image generation of the actual automobile based SAR radar system and the results analyzed using EM modeling or statistical distribution models are compared with this 3D point cloud scatterer model. The modeling target is selected as an bridge because it has the characteristic of having both water surface and ground terrain around the bridge and is also a target of great interest in both military and civilian use.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.4
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• pp.451-468
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• 2022

Leading enterprises in contract farming are in control of agricultural production and influence the structure of the agricultural system in contract areas. This study focuses on the contract farming of potatoes for manufacturing chips, which uses a high proportion of domestic raw materials, and identifies the characteristics of contract farming between confectionery company 𐩒 and potato farms in Haean-myeon, Yanggu-gun, Gangwon-do. This study also analyzes the impact of contract farming on local agriculture from the perspective of the industrialization of agriculture. The results of this study demonstrated that contracting companies ensured the quality of potatoes and smooth agricultural operations by first preferentially selecting farmhouses with land that is easy to work with and then supplying the necessary agricultural machinery to promote the intensification of their work. In addition, contracting companies influenced the centralization of the agriculture sector by selecting farmhouses capable of contracting over a certain scale and guaranteeing them sales channels and the specialization of potato farming in contract areas, mainly through the supply of processing varieties and the spread of cultivation technology. The results confirmed that these three dimensions of contract farming promoted the industrialization of local agriculture.

• Fisheries and Aquatic Sciences
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• v.26 no.2
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• pp.105-116
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• 2023

Improved fish post-harvest technologies (IFPT) have been promoted as more efficient methods of fish processing, preservation, and value addition than the traditional methods prevalent in developing countries. The adoption rates, however, do not appear to be convincing. The purpose of this study was to determine the socio-demographic and psychological factors that influence intention of Kenyan farmed fish traders to use IFPT. The technology acceptance model (TAM) was used to properly explain the impact of TAM constructs such as perceived usefulness (PU), perceived ease of use (PEOU), and attitude (ATT), as well as socio-demographic factors such as gender, age, education level and fish trading experience on traders' intention to use the technologies. A cross-sectional survey was conducted to collect data using a semi-structured questionnaire from 146 traders in Busia, Siaya and Kakamega counties. At a significance level of p = 0.05, a linear regression model was used to examine the socio-demographic and psychological determinants of the traders' behavioral intention to use the improved technologies. The regression analysis revealed that PU (β = 0.443; p = 0.000), PEOU (β = 0.364; p = 0.000) and ATT (β = 0.615; p = 0.000) influence traders' intention to use IFPT, with ATT having the highest influence on intention. However, the traders' socio-demographic characteristics have no effect on their intention to use the technologies, as the coefficients for gender (β = 0.148; p = 0.096), age (β = 0.016; p = 0.882), level of education (β = -0.135; p = 0.141) and fish trading experience (β = 0.017; p = 0.869) are all insignificant. These findings show that the traders intend to use IFPT and will use them when it is in their best economic interests.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.69-79
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• 2022

In this study, a method of generating and utilizing a normal map image used to represent the characteristics of the surface of an image material to improve the classification accuracy of the original material image is proposed. First of all, (1) to generate a normal map that reflects the surface properties of a material in an image, a U-Net with attention-R2 gate as a generator was used, and a Pix2Pix-based method using the generated normal map and the similarity with the original normal map as a reconstruction loss was used. Next, (2) we propose a network that can improve the accuracy of classification of the original material image by applying the previously created normal map image to the attention gate of the classification network. For normal maps generated using Pixar Dataset, the similarity between normal maps corresponding to ground truth is evaluated. In this case, the results of reconstruction loss function applied differently according to the similarity metrics are compared. In addition, for evaluation of material image classification, it was confirmed that the proposed method based on MINC-2500 and FMD datasets and comparative experiments in previous studies could be more accurately distinguished. The method proposed in this paper is expected to be the basis for various image processing and network construction that can identify substances within an image.