• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.11-17
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• 2020

In this paper, we propose a method to build a knowledge base based on natural language processing for intelligent battlefield awareness service. The current command and control system manages and utilizes the collected battlefield information and tactical data at a basic level such as registration, storage, and sharing, and information fusion and situation analysis by an analyst is performed. This is an analyst's temporal constraints and cognitive limitations, and generally only one interpretation is drawn, and biased thinking can be reflected. Therefore, it is essential to aware the battlefield situation of the command and control system and to establish the intellignet decision support system. To do this, it is necessary to build a knowledge base specialized in the command and control system and develop intelligent battlefield awareness services based on it. In this paper, among the entity names suggested in the exobrain corpus, which is the private data, the top 250 types of meaningful names were applied and the weapon system entity type was additionally identified to properly represent battlefield information. Based on this, we proposed a way to build a battlefield-aware knowledge base through mention extraction, cross-reference resolution, and relationship extraction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.337-339
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• 2015

As much interest recently location based service, a study to analyze the movement patterns of the users from getting a lot of large amounts of data collected from a GPS installed in the smart device. Also it is recorded in the log file in the form of day-to-day personal computer, the development of a variety of a smart phone, a black box, and navigation. This data is collected by the user have been developed a variety of personalized services. In this paper, using the black box camera, such as the vehicle to form a social network platform that broadcasts real-time traffic utilization real-time video information.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.8
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• pp.357-368
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• 2013

Our society has evolved into a fully connected society in a mixed reality environment enabling various knowledge sharing / management / control / creation due to the expansion of broadband ICT infrastructure, smart devices, cloud services and social media services. Therefore cyber threats have increased with the convenience. The society of the future can cause more complex and subtle problems, if you do not have an effective response to cyber threats, due to fusion of logical space and physical space, organic connection of the smart object and the universalization of fully connected society. In this paper, we propose the strategy to build knowledge-base as the basis to actively respond to new cyber threats caused by future various environmental changes and the universalization of fully connected society.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.333-340
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• 2011

The Human-Computer Interaction(HCI) is a study of the method for interaction between human and computers that merges the ergonomics and the information technology. The intelligent space, which is a part of the HCI, is an important area to provide effective user interface for the disabled, who are alienated from the information-oriented society. In the intelligent space for the disabled, the method supporting information depends on types of disability. In this paper, we only support the hearing-impaired. It is material to the gaze direction detection method because it is very efficient information provide method to present information on gazing direction point, except for the information provide location perception method through directly contact with the hearing-impaired. We proposed the gaze direction detection method must be necessary in order to provide the residence life application to the hearing-impaired like this. The proposed method detects the region of the user from multi-view camera images, generates candidates for directions of gaze for horizontal and vertical from each camera, and calculates the gaze direction of the user through the comparison with the size of each candidate. In experimental results, the proposed method showed high detection rate with gaze direction and foot sensing rate with user's position, and showed the performance possibility of the scenario for the disabled.

• The KIPS Transactions:PartA
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• v.10A no.5
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• pp.539-550
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• 2003

As the service of mobile internet has been expended, student users are increase. The computers have been widely used in a education field as the teaching tool by improvement of the multimedia contents processing and user interface. The English learning using the computers in the restricted education environment provides motivations and effective learning to learners, but still have some problem such as teaching and evaluating without consideration for differences of individual levels. In order to solve the problems and take the advantages, we propose the intelligent tutoring system for english learning with mobile technology. Overcoming limitations of the mobile environment and using proper treacher's roles,. We have applied the conventional estimation method of the intellectual learner level for students. Also, we have proposed the diagnostic function in order to determine the method of teaching-learing and item disposition that each leaner prefers. Then we have designed and implemented the expert module, providing the feedback for teaching, of the intelligent turoring system for mobile english learning. This system will be able to support the interaction between teachers and students and replace some roles of teacher in the mobile english learning.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.60-67
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• 2009

This paper describes a development of system that enables the user to simulate the rockfall and landslide from slopes using physical engine. Until now, it will not be able to accomplish the virtual experiment of three-dimensional interpretation about slope informations, stability evaluation, the rockfall and landslide simulation, etc., because of absence of three-dimensional simulation systems which relates with slopes. With like that reason, this paper developed a simulator which is identical or similar the rockfall and the landslide where the possibility which will occur or occurred from actuality is high very actual condition from virtual experiment. For a simulator development, this paper uses the physical engine which is mainly used from computer game and animation development etc., And it will be show the process where the rockfall and landslide occurs with simulator. This simulator which sees the process where the rockfall and the landslide occur from three-dimension computer graphics theory and the physical engine, is a system which is the possibility of showing actual feeling. Therefore, the result of this paper is applied in vehicle travelling guidance system and intelligence traffic systematic etc., because of creates visual service and three-dimensional application of the slope information database which is developed in existing, and will be able to forecast the upgrade of user benefit planning and a service.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.8
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• pp.253-262
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• 2023

E-voting is a concept that includes actions such as kiosk voting at a designated place and internet voting at an unspecified place, and has emerged to alleviate the problem of consuming a lot of resources and costs when conducting offline voting. Using E-voting has many advantages over existing voting systems, such as increased efficiency in voting and ballot counting, reduced costs, increased voting rate, and reduced errors. However, centralized E-voting has not received attention in public elections and voting on corporate agendas because the results of voting cannot be trusted due to concerns about data forgery and modulation and hacking by others. In order to solve this problem, recently, by designing an E-voting system using blockchain, research has been actively conducted to supplement concepts lacking in existing E-voting, such as increasing the reliability of voting information and securing transparency. In this paper, we proposed an electronic voting system that introduced hybrid blockchain that uses public and private blockchains in convergence. A hybrid blockchain can solve the problem of slow transaction processing speed, expensive fee by using a private blockchain, and can supplement for the lack of transparency and data integrity of transactions through a public blockchain. In addition, the proposed system is implemented as BaaS to ensure the ease of type conversion and scalability of blockchain and to provide powerful computing power. BaaS is an abbreviation of Blockchain as a Service, which is one of the cloud computing technologies and means a service that provides a blockchain platform ans software through the internet. In this paper, in order to evaluate the feasibility, the proposed system and domestic and foreign electronic voting-related studies are compared and analyzed in terms of blockchain type, anonymity, verification process, smart contract, performance, and scalability.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.136-142
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• 2006

Existing buffer management schemes for shared-memory output queueing switches can be classified into two types: In the first type, some constant amount of memory space is guaranteed to each virtual queue using static queue thresholds. The static threshold method (ST) belongs to this type. On the other hand, the second type of approach tries to maximize the buffer utilization in 머 locating buffer memories. The complete sharing (CS) method is classified into this type. In the case of CS, it is very hard to protect regular traffic from mis-behaving traffic flows while in the case of ST the thresholds can not be adjusted according to varying traffic conditions. In this paper, we propose a new buffer management method called weighted dynamic thresholds (WDT) which can process packet flows based on loss priorities for quality-of-service (QoS) functionalities with fairly high memory utilization factors. We verified the performance of the proposed scheme through computer simulations.