• Journal of the Korea Society of Computer and Information
• /
• v.27 no.12
• /
• pp.9-16
• /
• 2022

In this paper, we propose a plan to design and utilize the RDBS(Record Block Data file management System) so that data can be recovered when data files in the Combat Management System are mismatched. The CMS(Combat Management System) manages the same files in multiple IPN(Infomation Processing Node) repositories to support multiplexing. However, mismatches in data files can occur due to equipment maintenance or user immaturity. The existing CMS does not manage the history of changes in data files, and when a mismatch occurs, data file were synchronized based on the latest date. But, It is difficult to say that files with the latest date have the highest reliability, and once the file synchronization has progressed, it cannot be recovered with pre-synchronization data. To solve this problem, data was stored and synchronized in units of record blocks using RDBS proposed in this paper, and the Rsync algorithm was used to reduce the overhead of file synchronization due to units of record blocks. SW applied with RDBS was tested for performance in a simulated environment, and it was confirmed that it could be applied to CMS through normal operation confirmation.

• Journal of the Korea Convergence Society
• /
• v.12 no.11
• /
• pp.221-228
• /
• 2021

This study was conducted to improve the convenience of design of mobile VR devices use in a way binds smart phones. Research on traditional mobile VR devices is insufficient. So the first survey was conducted on users 100 to understand the current status and status of mobile VR devices. As a result, it was found that the satisfaction with the convenience of use was significantly lowered, and countermeasures were needed. Then, a second survey of 30 Heavy Users was conducted to find out specific usability and problems of mobile VR devices. Through this, problems, ease of use, and other opinions of mobile VR devices were found. The survey results were analyzed through the Descriptive Statistics Act, and it was found that improvement was urgent due to low satisfaction with wearing and network. In-depth interviews were conducted with the same respondents. As with the problems derived first, problems such as wearing satisfaction, excessive head weight for long-term use, and lack of content could be found. Based on the previous studies, the focus group interview consisting of 6 experts derived the ease of use evaluation element. It consists of elements that can satisfy the convenience of use of mobile VR devices for creation, wearing satisfaction, network, morphology, learning, and spatiality, and has a total of 26. Using this evaluation elements, it is intended to provide better ease of use to users who will use the mobile VR device.

• The Journal of the Acoustical Society of Korea
• /
• v.41 no.3
• /
• pp.359-366
• /
• 2022

In this paper, we present the Korean menu-ordering Sentence Text-to-Speech (TTS) system using conformer-based FastSpeech2. Conformer is the convolution-augmented transformer, which was originally proposed in Speech Recognition. Combining two different structures, the Conformer extracts better local and global features. It comprises two half Feed Forward module at the front and the end, sandwiching the Multi-Head Self-Attention module and Convolution module. We introduce the Conformer in Korean TTS, as we know it works well in Korean Speech Recognition. For comparison between transformer-based TTS model and Conformer-based one, we train FastSpeech2 and Conformer-based FastSpeech2. We collected a phoneme-balanced data set and used this for training our models. This corpus comprises not only general conversation, but also menu-ordering conversation consisting mainly of loanwords. This data set is the solution to the current Korean TTS model's degradation in loanwords. As a result of generating a synthesized sound using ParallelWave Gan, the Conformer-based FastSpeech2 achieved superior performance of MOS 4.04. We confirm that the model performance improved when the same structure was changed from transformer to Conformer in the Korean TTS.

• Journal of the Korean Society for Library and Information Science
• /
• v.57 no.1
• /
• pp.93-114
• /
• 2023

Information extraction can facilitate the intensive analysis of documents by providing semantic triples which consist of named entities and their relations recognized in the texts. However, most of the research so far has been carried out separately for named entity recognition and relation extraction as individual studies, and as a result, the effective performance evaluation of the entire information extraction systems was not performed properly. This paper introduces two models of end-to-end information extraction that can extract various entity names in clinical records and their relationships in the form of semantic triples, namely pipeline and joint models and compares their performances in depth. The pipeline model consists of an entity recognition sub-system based on bidirectional GRU-CRFs and a relation extraction module using multiple encoding scheme, whereas the joint model was implemented with a single bidirectional GRU-CRFs equipped with multi-head labeling method. In the experiments using i2b2/VA 2010, the performance of the pipeline model was 5.5% (F-measure) higher. In addition, through a comparative experiment with existing state-of-the-art systems using large-scale neural language models and manually constructed features, the objective performance level of the end-to-end models implemented in this paper could be identified properly.

• Analytical Science and Technology
• /
• v.21 no.2
• /
• pp.93-101
• /
• 2008

The application of solid phase microextraction (SPME) is generally conducted by directly immersing the fiber into the liquid sample or by exposing the fiber in the head space (HS). The extraction temperature, the time of incubation, and application of stirring are often designated to be the most important parameters for achieving the best extraction efficiencies of HS-SPME analysis. In this study, relative importance of these three analytical parameters involved in the HS-SPME method is evaluated using a polydimethylsiloxane/carboxen (PDMS/CAR) fiber. To optimize its operation conditions the competing relationships between different parameters were investigated by comparing the extraction efficiency based on the combination of three parameters and two contracting conditions: (1) heating the sample at 30 vs. 50 C, (2) exposing samples at two durations of 10 vs. 30 min, and (3) application of stirring vs. no stirring. According to our analysis among 8 combination types of HS-SPME method, an extraction condition termed as S50-30 condition ((1) 1200 rpm stirring, (2) $50^{\circ}C$ exposure temp, and (3) 30 min exposure duration) showed maximum recovery rate of 45.5~68.5% relative to an arbitrary reference of direct GC injection. According to this study, the employment of stirring is the most crucial factor to improve extraction efficiency in the application of HS-SPME.

• Journal of Korea Society of Industrial Information Systems
• /
• v.28 no.5
• /
• pp.15-30
• /
• 2023

Recently, generative models based on the Transformer architecture, such as ChatGPT, have been gaining significant attention. The Transformer architecture has been applied to various neural network models, including Google's BERT(Bidirectional Encoder Representations from Transformers) sentence generation model. In this paper, a method is proposed to create a text binary classification model for determining whether a comment on Korean movie review is positive or negative. To accomplish this, a pre-trained multilingual BERT sentence generation model is fine-tuned and transfer learned using a new Korean training dataset. To achieve this, a pre-trained BERT-Base model for multilingual sentence generation with 104 languages, 12 layers, 768 hidden, 12 attention heads, and 110M parameters is used. To change the pre-trained BERT-Base model into a text classification model, the input and output layers were fine-tuned, resulting in the creation of a new model with 178 million parameters. Using the fine-tuned model, with a maximum word count of 128, a batch size of 16, and 5 epochs, transfer learning is conducted with 10,000 training data and 5,000 testing data. A text sentiment binary classification model for Korean movie review with an accuracy of 0.9582, a loss of 0.1177, and an F1 score of 0.81 has been created. As a result of performing transfer learning with a dataset five times larger, a model with an accuracy of 0.9562, a loss of 0.1202, and an F1 score of 0.86 has been generated.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.6
• /
• pp.1077-1086
• /
• 2023

The searchable encryption model allows to selectively search for encrypted data stored on a remote server. In a real-world scenarios, the model must be able to support multiple search keywords, multiple data owners/users. In this paper, these models are referred to as Multi Ranked Searchable Encryption model. However, at the time this paper was written, the proposed models use fully-trusted trapdoor centers, some of which assume that the connection between the user and the trapdoor center is secure, which is unlikely that such assumptions will be kept in real life. In order to improve the practicality and security of these searchable encryption models, this paper proposes a new Multi Ranked Searchable Encryption model which uses random keywords to protect search words requested by the data downloader from an honest-but-curious trapdoor center with an external attacker without the assumptions. The attacker cannot distinguish whether two different search requests contain the same search keywords. In addition, experiments demonstrate that the proposed model achieves reasonable performance, even considering the overhead caused by adding this protection process.

• Tunnel and Underground Space
• /
• v.33 no.6
• /
• pp.519-533
• /
• 2023

In this study, the linear cutting tests of pick cutters were carried out on a granitic rock with the average compressive strength over 100 MPa. From the tests, the correlation between the cutter acting force and the temperature measured by infrared thermal imaging camera during rock cutting was analyzed. In every experimental condition, the maximum temperature was measured at the rock surface where the chipping occurred, and the temperature generated in the rock was closely correlated with the cutter acting force. On the other hand, the temperature of a pick cutter increased up to only 36℃ above the ambient temperature, and the correlation with the cutter force was not obvious. This can be attributed to the short cutting distance under laboratory conditions and the high thermal conductivity of the tungsten carbide inserts. However, the relatively high temperature of the tungsten carbide inserts was found to be maintained. Therefore, it is recommended that a reinforcement between the insert and the head of a pick cutter or the quality improvement of silvering brazing in the production of a cutter is necessary to maintain the high cutting performance of a pick cutter.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.34 no.3
• /
• pp.393-401
• /
• 2024

Recently, the Internet of Things (IoT) has been widely used in industries and daily life that directly affect human safety, life, and assets. However, IoT devices, which need to meet low-cost, lightweight, and low-power requirements, face a significant problem of shortened battery lifetime due to battery draining attacks and interference. To solve this problem, the 802.11ba standard for the Wake-up Receiver (WuR) has emerged, this feature is playing a crucial role in minimizing energy consumption. However, the WuR protocol did not consider security mechanisms in order to reduce latency and overhead. Therefore, in this study, anAdaptive Power Saving Mechanism (APSM) is proposed for low-power WuR to counter battery draining attacks. APSM can minimize abnormally occurring power consumption by exponentially increasing power-saving time in environments prone to attacks. According to experimental results, the proposed APSM improved energy consumption efficiency by a minimum of 13.77% compared to the traditional Legacy Power Saving Mechanism (LPSM) when attack traffic ratio is 10% or more of the total traffic.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.2
• /
• pp.48-59
• /
• 2024

As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.