• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.25-29
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• 2021

The strip binary is a binary from which debug symbol information has been deleted, and therefore it is difficult to analyze the binary through techniques such as reverse engineering. Traditional binary analysis tools rely on debug symbolic information to analyze binaries, making it difficult to detect or analyze malicious code with features of these strip binaries. In order to solve this problem, the need for a technology capable of effectively extracting the information of the strip binary has emerged. In this paper, focusing on the fact that the byte code of the binary file is generated very differently depending on compiler version, optimazer level, etc. For effective compiler version extraction, the entire byte code is read and imaged as the target of the stripped binaries and this is applied to the convolution neural network. Finally, we achieve an accuracy of 93.5%, and we provide an opportunity to analyze stripped binary more effectively than before.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.501-510
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• 2021

This paper was described to assess the possibility of field application of the developed textbooks by developing a textbook using action learning to conduct ICT electromagnetic waves training education for elementary and middle school teachers and applying it on a pilot basis. To this end, it was organized to be used in conjunction with classes in several subjects and provided various teaching materials to facilitate teachers' convenience to use the textbook. Also, the textbook was composed of a content structure of 10 sessions. As for the proposed textbook, the overall understanding of ICT electromagnetic waves was improved through pilot application in teacher training, and problem-solving ability, cooperative learning ability, democratic citizenship, and knowledge information processing ability were improved through action learning. It can be said that it contributed to improving the understanding of ICT electromagnetic waves and teachers' competencies.

• Design & Manufacturing
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• v.15 no.4
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• pp.43-50
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• 2021

Pump-cap is a device for discharging the contents stored inside a container to the outside of the container by a simple operation by a certain amount. In particular, in recent years, as the number of cosmetic products made of functional materials has rapidly increased, the development of convenient containers for functional materials is being actively conducted. Among these, there are a growing number of products that show their efficacy only by mixing two components, so the development of a dual pump cap container is necessary. However, the conventional dual pump cap container has a problem in that it is difficult to implement a quantitative discharge as solutions having different viscosities are used. Therefore, in this study, a discharge port of a dual pump cap that can apply an optimal ratio was designed by analyzing the discharge amount of two components with different viscosities through computational fluid dynamics. Since the discharge amount is affected by the size of the discharge port, the higher the viscosity of the solution, the larger the discharge port should be set. Conversely, the lower the viscosity, the smaller the discharge port should be. Through this, it is possible to dispense a fixed amount of a heterogeneous solution by one pumping, and it is determined that the user's convenience will increase.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.94-103
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• 2022

As a large amount of data generated by sensors or devices at the manufacturing site is transmitted to the server or client, problems arise in network processing time delay and storage resource cost increase. To solve this problem, considering the manufacturing site, where real-time responsiveness and non-disruptive processes are essential, QRC (Quotient Remainder Compression) and BL_beta compression algorithms that enable real-time and lossless compression were applied to actual manufacturing site sensor data for the first time. As a result of the experiment, BL_beta had a higher compression rate than QRC. As a result of experimenting with the same data by slightly adjusting the data size of QRC, the compression rate of the QRC algorithm with the adjusted data size was 35.48% and 20.3% higher than the existing QRC and BL_beta compression algorithms.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.123-130
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• 2022

During the past decades, detection of gear defects remains as a major problem, especially when the gears are subject to non-stationary phenomena. The idea of this paper is to mixture a multilevel wavelet transform with a fast EMD decomposition in order to early detect gear defects. The sensitivity of a kurtosis is used as an indicator of gears defect burn. When the gear is damaged, the appearance of a crack on the gear tooth disrupts the signal. This is due to the presence of periodic pulses. Nevertheless, the existence of background noise induced by the random excitation can have an impact on the values of these temporal indicators. The denoising of these signals by multilevel wavelet transform improves the sensitivity of these indicators and increases the reliability of the investigation. Finally, a defect diagnosis result can be obtained after the fast transformation of the EMD. The proposed approach consists in applying a multi-resolution wavelet analysis with variable decomposition levels related to the severity of gear faults, then a fast EMD is used to early detect faults. The proposed mixed methods are evaluated on vibratory signals from the test bench, CETIM. The obtained results have shown the occurrence of a teeth defect on gear on the 5th and 8th day. This result agrees with the report of the appraisal made on this gear system.

• Journal of agricultural medicine and community health
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• v.47 no.1
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• pp.1-13
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• 2022

Objectives: This study aimed to establish a linkage model involving regional responsible medical institutions after analyzing the existing conditions and deriving problems through qualitative analysis within the community care system. Methods: A total of 14 participants of this study were selected through the snowball sampling method, including 7 community care service providers and 7 service users. As for the research data, primary data were collected through interviews, and as a result of analyzing according to Aday&Anderson' model, a total of 5 catergories, 8 topics, and 22 sub theme were derived. Results: The problem derived from the interview is that division services are provided for each institution due to the absence of a key central institution of community care system, and users' commercial institutions is unclear. The second is the inconsistency between the needs and supply for community care, resulting in a possibility of delay in returning to the community after discharge. Based on these problems, it is necessary to unify it as an community care window of the Dong-community center. In addition, there is a need for public health centers to play an active role, and to establish a public-private joint system with the Health and Living Support Center to establish a model that can play a certain role. Conclusions: Therefore, based on the results of this study, it can be used as basic data when constructing community care model and applying it as an expanded model in the future.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.162-176
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• 2021

Recently, Autonomous Vehicle(AV) has been expected to solve various transportation problems. s the problem of environmental pollution become serious, research to reduce pollution is needed. However, empirical research on AV related pollution is insufficient. Based on this background, this study analyzed network performance changes and CO₂ emissions introduc AVs and Electric Vehicles(EV) in eight intersections. The results show that when AVs with internal combustion engines were, the effect of carbon reduction over the network was insignificant. On the other hand, it was that the total amount of CO₂ generated in the network decreased significantly when EVs and autonomous electric vehicles were emissions in the transportation sector.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.24-29
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• 2022

Semantic role labeling task used in various natural language processing fields, such as information extraction and question answering systems, is the task of identifying the arugments for a given sentence and predicate. Predicate used as semantic role labeling input are extracted using lexical analysis results such as POS-tagging, but the problem is that predicate can't extract all linguistic patterns because predicate in korean language has various patterns, depending on the meaning of sentence. In this paper, we propose a korean predicate recognition method using neural network model with pre-trained embedding models and lexical features. The experiments compare the performance on the hyper parameters of models and with or without the use of embedding models and lexical features. As a result, we confirm that the performance of the proposed neural network model was 92.63%.

• Journal of Korean Critical Care Nursing
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• v.15 no.1
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• pp.1-12
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• 2022

Purpose : This study aimed to identify the status of intrahospital transport (IHT) of critically ill patients and provide baseline data to form recommendations for safer transport. Methods : Data were collected from 141 intensive care unit (ICU) nurses who attended national conferences between February and August, 2018. The collected data were analyzed using descriptive statistics and ANOVA, and post-hoc analysis was conducted with the Scheffé and Games-Howell tests. Results : Of the nurses surveyed, 61.7% answered that their workplace had a transport guideline. In terms of the experience of ICU nurses, 31.2% of respondents answered that they had received training on IHT. This result indicated that the degree of implementation of the guidelines for IHT was generally high, but some, including guidelines on personnel, equipment, and monitoring, were not. Guidelines of IHT were well observed when the institutes had specific guidelines for IHT of critically ill patients with specified transport grades, a scoring system to assess stability of a patient, a checklist and a protocol for action in case of problems, and when healthcare providers were provided with training. Conclusion : These results suggest that organized infrastructure, such as a transport guideline with transport grades, a checklist to improve the implementation of guidelines, and a protocol for coping with a problem, should be provided for safe transport. Additionally, effective education and evaluation to improve the competency of staff participating in the transport of patients will help reduce the occurrence of adverse events in intensive care transport in hospitals and promote patient safety.