• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.64-79
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• 2023

This study reviewed the process of applying the Monte Carlo simulation technique to the traffic allocation problem of metropolitan subways. The analysis applied the assumption of a normal distribution in which the travel time information of the inter-station sample is the basis of the probit model. From this, the average and standard deviation are calculated by separating the traffic between stations. A plan was proposed to apply the simulation with the weights of the in-vehicle time of individual links and the walking and dispatch interval of transfer. Long-distance traffic with a low number of samples of 50 or fewer was evaluated as a way to analyze the characteristics of similar traffic. The research results were reviewed in two directions by applying them to the Seoul Metropolitan Subway Network. The travel time between single stations on the Seolleung-Seongsu route was verified by applying random sampling to the in-vehicle time and transfer time. The assumption of a normal distribution was accepted for sample sizes of more than 50 stations according to the inter-station traffic sample of the entire Seoul Metropolitan Subway. For long-distance traffic with samples numbering less than 50, the minimum distance between stations was 122Km. Therefore, it was judged that the sample deviation equality was achieved and the inter-station mean and standard deviation of the transport card data for stations at this distance could be applied.

• Journal of the Korean Electrochemical Society
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• v.25 no.4
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• pp.174-183
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• 2022

Polymer electrolyte fuel cells and water electrolysis are attracting attention in terms of high energy density and high purity hydrogen production. The catalyst layer for the polymer electrolyte fuel cell and water electrolysis is a porous electrode composed of a precious metal-based electrocatalyst and an ionomer binder. Among them, the ionomer binder plays an important role in the formation of a three-dimensional network for ion conduction in the catalyst layer and the formation of pores for the movement of materials required or generated for the electrode reaction. In terms of the use of commercial perfluorinated ionomers, the content of the ionomer, the physical properties of the ionomer, and the type of the dispersing solvent system greatly determine the performance and durability of the catalyst layer. Until now, many studies have been reported on the method of using an ionomer for the catalyst layer for polymer electrolyte fuel cells. This review summarizes the research results on the use of ionomer binders in the fuel cell aspect reported so far, and aims to provide useful information for the research on the ionomer binder for the catalyst layer, which is one of the key elements of polymer electrolyte water electrolysis to accelerate the hydrogen economy era.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.387-398
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• 2023

The COVID-19 outbreak has significantly impacted human lifestyles and patterns. It was recommended to avoid face-to-face contact and over-crowded indoor places as much as possible as COVID-19 spreads through air, as well as through droplets or aerosols. Therefore, if a person who has contacted a COVID-19 patient or was at the place where the COVID-19 patient occurred is concerned that he/she may have been infected with COVID-19, it can be fully expected that he/she will search for COVID-19 symptoms on Google. In this study, an exploratory data analysis using deep learning models(DNN & LSTM) was conducted to see if we could predict the number of confirmed COVID-19 cases by summoning Google Trends, which played a major role in surveillance and management of influenza, again and combining it with data on the number of confirmed COVID-19 cases. In particular, search term frequency data used in this study are available publicly and do not invade privacy. When the deep neural network model was applied, Seoul (9.6 million) with the largest population in South Korea and Busan (3.4 million) with the second largest population recorded lower error rates when forecasting including search term frequency data. These analysis results demonstrate that search term frequency data plays an important role in cities with a population above a certain size. We also hope that these predictions can be used as evidentiary materials to decide policies, such as the deregulation or implementation of stronger preventive measures.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.53-61
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• 2010

In wireless sensor networks(WSNs) individual sensor nodes are subject to security compromises. An adversary can physically capture sensor nodes and obtain the security information. And the adversary injects false reports into the network using compromised nodes. If undetected, these false reports are forwarded to the base station. False reports injection attacks can not only result in false alarms but also depletion of the limited amount of energy in battery powered sensor nodes. To combat these false reports injection attacks, several filtering schemes have been proposed. The statistical en-routing filtering(SEF) scheme can detect and drop false reports during the forwarding process. In SEF, The number of the message authentication codes(threshold) is important for detecting false reports and saving energy. In this paper, we propose a dynamic threshold determination method for energy efficient SEF using fuzzy-logic in wireless sensor networks. The proposed method consider false reports rate and the number of compromised partitions. If low rate of false reports in the networks, the threshold should low. If high rate of false reports in networks, the threshold should high. We evaluated the proposed method’s performance via simulation.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.189-198
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• 2023

As crimes frequently occur on the street, the spread of CCTV is increasing. However, due to the shortcomings of passively operated CCTV, the need for intelligent CCTV is attracting attention. Due to the heavy system of such intelligent CCTV, high-performance devices are required, which has a problem in that it is expensive to replace the general CCTV. To solve this problem, an intelligent CCTV system that recognizes low-quality images and operates even on devices with low performance is required. Therefore, this paper proposes a Saying CCTV system that can detect threats in real time by using the AWS cloud platform to lighten the system and convert images into text. Based on the data extracted using YOLO v4 and OpenPose, it is implemented to determine the risk object, threat behavior, and threat situation, and calculate the risk using machine learning. Through this, the system can be operated anytime and anywhere as long as the network is connected, and the system can be used even with devices with minimal performance for video shooting and image upload. Furthermore, it is possible to quickly prevent crime by automating meaningful statistics on crime by analyzing the video and using the data stored as text.

• Journal of Korean Clinical Nursing Research
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• v.29 no.1
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• pp.24-41
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• 2023

Purpose: This study aimed to update the previously published nursing practice guideline for prevention of venous thromboembolism (VTE). Methods: The guideline was updated according to the manuals developed by National Institute for Health and Care Excellence (NICE) and Scottish Intercollegiate Guidelines Network (SIGN), and a Handbook for Clinical Practice Guideline Developer Version 10. Results: The updated nursing practice guideline for prevention of VTE was consisted of 16 domains, 46 subdomains, and 216 recommendations. The recommendations in each domain were: 4 general issues, 8 assessment of risk and bleeding factors, 5 interventions for prevention of VTE, 18 mechanical interventions, 36 pharmacological interventions, 36 VTE prevention starategies for medical patients, 25 for cancer patients, 13 for pregnancy, 8 for surgical patients, 7 for thoractic and cardiac surgery, 16 for orthopedic surgery, 10 for cranial and spinal surgery, 5 for vascular surgery, 13 for other surgery, 3 educations and information, and 2 documentation and report. For these recommendations, the level of evidence was 32.1% for level I, 51.8% for level II, and 16.1% for level III according to the infectious diseases society of America (IDSA) rating system. A total of 112 new recommendations were developed and 49 previous recommendations were deleted. Conclusion: The updated nursing practice guideline for prevention of VTE is expected to serve as an evidence-based practice guideline for prevention of VTE in South Korea. It is recommended that this guideline will disseminate to clinical nursing settings nationwide to improve the effectiveness of prevention of VTE practice.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.36-44
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• 2022

This study analyzed Corporate Social Responsibility (CSR hereafter) of construction companies, especially focusing on small-medium companies. Surveys on construction companies' CSR activities and intensive interview with experts were executed to analyze and draw some implications. The empirical results shows that most of construction companies feel keenly necessity of CSR. The level of small-medium companies' awareness is lower than that of large companies'. CEO and board members of small-medium companies are less concerned about CSR yet. Carrying CSR activities co-working with NGO is preferred because of expertise and network. This study also suggests future strategy for CSR in the construction industry, focusing on small-medium companies. First of all it is necessary for small-medium companies to enhance the level of awareness and encourage participation in CSR. Second, it is required to strengthen relation with NGO and share information for CSR. Third, it is really significant to strengthen budget support and secure personnel to activate CSR. The systematic support by gevernment is also important. Lastly, it is essential to mount a publicity campaign and develop various CSR program. The role of 'Construction Industry Foundation for Social Responsibility' is emphasized because small-medium companies have very little room for CSR.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.119-125
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• 2023

IT infrastructure operation has advanced, and the methods for managing systems have become widely adopted. Recently, research has focused on improving system management using Syslog. However, utilizing log data collected through these methods presents challenges, as logs are extracted in various formats that require expert analysis. This paper proposes a system that utilizes edge computing to distribute the collection of Syslog data and preprocesses duplicate data before storing it in a central database. Additionally, the system constructs a data dictionary to classify and count data in real-time, with restrictions on transmitting registered data to the central database. This approach ensures the maintenance of predefined patterns in the data dictionary, controls duplicate data and temporal duplicates, and enables the storage of refined data in the central database, thereby securing fundamental data for big data analysis. The proposed algorithms and procedures are demonstrated through simulations and examples. Real syslog data, including extracted examples, is used to accurately extract necessary information from log data and verify the successful execution of the classification and storage processes. This system can serve as an efficient solution for collecting and managing log data in edge environments, offering potential benefits in terms of technology diffusion.