• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.4
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• pp.403-408
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• 2015

A text-based communication system has been developed with a communication function on AIS and display and input function on ECDIS as a way to complement voice communication. It features no linguistic error and is not affected by VHF restrictions on use and noise. The text communication system is designed to use messages for clear intentions and further improves convenience of users by using various UI through software. It works without additional hardware installation and modification and can transmit a sentence by selecting only via Message Banner Interface without keyboard input and furthermore has a advantage to enhance processing speed through its own message coding and decoding. It is determined as the most useful alternative to reduce language limitations and recognition errors of the user and solve the problem of various voice communications on VHF. In addition, it will help to prevent collisions between ships with decrease in VHF use, accurate communication and request of cooperation based on text at heavy traffic areas.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.374-376
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• 2022

The Malware App Analysis Tool is a system that analyzes Android-based apps by the AI-based algorithm defined in the tool and detects whether malware code is included. Currently, as the spred of smartphones is activated, crimes using malware apps have increased, and accordingly, security for malware apps is required. Android operating systems used in smartphones have a share of more than 70% and are open-source-based, so not only will there be many vulnerabilities and malware, but also more damage to malware apps, increasing demand for tools to detect and analyze malware apps. However, this paper is proposed because there are many difficulties in designing and developing a malware app analysis tool because the security functional requirements for the malware app analysis tool are not clearly specified. Through the developed protection profile, technology can be improved based on the design and development of malware app analysis tools, safety can be secured by minimizing damage to malware apps, and furthermore, trust in malware app analysis tools can be guaranted through common criteria.

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.73-82
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• 2019

Airport is shifted airport 1.0 to airport 4.0 called smart airport and services paradigm is changed into direction to point the customer targeted benefits. Smart airports make use of integrated Internet of Things components to provide added-value services. By integrating smart components, airports are being exposed to a larger attack surface and new attack vectors. Self-services such as web or mobile check-in, self check-in/tagging/back drop/boarding, etc. should be strengthened to make airport processes smarter, and technologies such as automatic immigration, smart security search, and automatic AI-based baggage search should be applied. In this paper, we describe the necessity and importance of smart airports and next generation security screening technology. Further, we describe a walk through-type smart security screening system.

• Journal of Platform Technology
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• v.11 no.6
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• pp.3-12
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• 2023

In today's system operation, it is difficult to detect failures and take immediate action in the case of a shortage of manpower compared to the number of equipment or failures in vulnerable time zones, which can lead to delays in failure recovery. In addition, various algorithms exist to detect abnormal symptom data, and it is important to select an appropriate algorithm for each problem. In this paper, an ensemble-based isolation forest model was used to efficiently detect multivariate point anomalies that deviated from the mean distribution in the data set generated to predict system failure and minimize service interruption. And since significant changes in memory space usage are observed together with changes in CPU usage, the problem is solved by using LSTM-Auto Encoder for a collective anomaly in which another feature exhibits an abnormal pattern according to a change in one by comparing two or more features. did In addition, evaluation indicators are set for the performance evaluation of the model presented in this study, and then AI model evaluation is performed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.89-95
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• 2024

Nowadays, the competitiveness of a company depends on the ability of all organizational members to share and utilize the organizational knowledge accumulated by the organization. As if to prove this, the world is now focusing on ChetGPT service using generative AI technology based on LLM (Large Language Model). However, it is still difficult to apply the ChetGPT service to work because there are many hallucinogenic problems. To solve this problem, sLLM (Lightweight Large Language Model) technology is being proposed as an alternative. In order to construct sLLM, corporate data is essential. Corporate data is the organization's ERP data and the company's office document knowledge data preserved by the organization. ERP Data can be used by directly connecting to sLLM, but office documents are stored in file format and must be converted to data format to be used by connecting to sLLM. In addition, there are too many technical limitations to utilize office documents stored in file format as organizational knowledge information. This study proposes a method of storing office documents in DB format rather than file format, allowing companies to utilize already accumulated office documents as an organizational knowledge system, and providing office documents in data form to the company's SLLM. We aim to contribute to improving corporate competitiveness by combining AI technology.

• Structural Monitoring and Maintenance
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• v.7 no.1
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• pp.59-67
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• 2020

This study aims to develop the fuzzy risk assessment model of the debris flow to verify the accuracy of risk assessment in order to help related organizations reduce losses caused by landslides. In this study, actual cases of landslides that occurred are utilized as the database. The established models help us assess the occurrence of debris flows using computed indicators, and to verify the model errors. In addition, comparisons are made between the models to determine the best one to use in practical applications. The results prove that the risk assessment model systems are quite suitable for debris flow risk assessment. The reproduction consequences of highlight point discovery are shown in highlight guide coordinating toward discover steady and coordinating component focuses and effectively identified utilizing these two systems, by examining the variety in the distinguished highlights and the element coordinating.

• Journal of Energy Engineering
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• v.27 no.4
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• pp.86-91
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• 2018

This paper is a review of accounts balancing time in small distributed power trading platform using blockchain technology. First, the national VPP energy management system using the AMI applied to this study is introduced and then the accounts balancing time and process of the cryptocurrency coin payment which based on the power generation of pro-consumer certified by power big data analysis in a test bed environment is discussed. Futhermore the configuration of a power Big Data analysis system with GPU Fast Big Data that applies MapD to current lambda architecture is also introduced.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.2
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• pp.151-158
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• 2019

Wireless communication technology (WAVE) for vehicles, which is the core technology behind the next-generation intelligent transport system (C-ITS), is used to deliver information about vehicles to prevent traffic accidents and traffic situations that may arise between vehicles and infrastructure. Similar traffic issues often arise in marine scenarios. Currently, AIS is being used as a means of transmitting information such as the status of relative vessels, but research is being carried out to solve problems with AIS such as overloading by applying wireless communication technology for vehicles to the sea. In this study, a collision warning system suitable for small-sized vessels was developed based on the marine application of WAVE for vehicles verified through prior research, and the adequacy of this collision warning system was reviewed through a practical test. It is expected that this system will contribute greatly to future e-Navigation applications or self-driving ships as well as to preventing marine accidents.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.795-835
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• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.91-103
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• 2022

Determination of sound source characteristics such as: sound volume, direction and distance to the source is one of the important techniques for unmanned systems like autonomous vehicles, robot systems and AI speakers. There are multiple methods of determining the direction and distance to the sound source, e.g., using a radar, a rider, an ultrasonic wave and a RF signal with a sound. These methods require the transmission of signals and cannot accurately identify sound sources generated in the obstructed region due to obstacles. In this paper, we have implemented and evaluated a method of detecting and identifying the sound in the audible frequency band by a method of recognizing the volume, direction, and distance to the sound source that is generated in the periphery including the invisible region. A cross-shaped based sound source recognition algorithm, which is mainly used for identifying a sound source, can measure the volume and locate the direction of the sound source, but the method has a problem with "blind spots". In addition, a serious limitation for this type of algorithm is lack of capability to determine the distance to the sound source. In order to overcome the limitations of this existing method, we propose a QRAS-based algorithm that uses rectangular-shaped technology. This method can determine the volume, direction, and distance to the sound source, which is an improvement over the cross-shaped based algorithm. The QRAS-based algorithm for the OSSD uses 6 AITDs derived from four microphones which are deployed in a rectangular-shaped configuration. The QRAS-based algorithm can solve existing problems of the cross-shaped based algorithms like blind spots, and it can determine the distance to the sound source. Experiments have demonstrated that the proposed QRAS-based algorithm for OSSD can reliably determine sound volume along with direction and distance to the sound source, which avoiding blind spots.