• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.65-72
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• 2020

Today, the Internet of Things is used in many places, including homes, industrial sites, and hospitals, to give us convenience. Many services generate new value through real-time data collection, storage and analysis as devices are connected to the network. Many of these fields are creating services and applications that utilize sensors and communication functions within IoT devices. However, since everything can be hacked, it causes a huge privacy threat to users who provide data. For example, a variety of sensitive information, such as personal information, lifestyle patters and the existence of diseases, will be leaked if data generated by smarwatches are abused. Development of IoT must be accompanied by the development of security. Recently, Differential Privacy(DP) was adopted to privacy-preserving data processing. So we propose the method that can aggregate health data safely on smartwatch platform, based on DP.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.9-17
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• 2021

Humans are social animals that have gained information or social interaction through dialogue. In conversation, the mood of the word can change depending on the sensibility of one person to another. Relationships between characters in films are essential for understanding stories and lines between characters, but methods to extract this information from films have not been investigated. Therefore, we need a model that automatically analyzes the relationship aspects in the movie. In this paper, we propose a method to analyze the relationship between characters in the movie by utilizing deep learning techniques to measure the emotion of each character pair. The proposed method first extracts main characters from the movie script and finds the dialogue between the main characters. Then, to analyze the relationship between the main characters, it performs a sentiment analysis, weights them according to the positions of the metabolites in the entire time intervals and gathers their scores. Experimental results with real data sets demonstrate that the proposed scheme is able to effectively measure the emotional relationship between the main characters.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.12
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• pp.311-318
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• 2021

With the rapid development of the Internet of Things(IoT) technology recently, multi-access edge computing(MEC) is emerged as a next-generation technology for real-time and high-performance services. High mobility of users between MECs with limited service areas is considered one of the issues in the MEC environment. In this paper, we consider a vehicle edge computing(VEC) environment which has a high mobility, and propose a task migration algorithm to decide whether or not to migrate and where to migrate using DQN, as a reinforcement learning method. The objective of the proposed algorithm is to improve the system throughput while satisfying QoS(Quality of Service) requirements by minimizing the difference between queueing delays in vehicle edge computing servers(VECSs). The results show that compared to other algorithms, the proposed algorithm achieves approximately 14-49% better QoS satisfaction and approximately 14-38% lower service blocking rate.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.21-30
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• 2021

Unlike behaviors that are generally known, information regarding unspecific behaviors is insufficient. For an education or guidance regarding the unspecific behaviors, collection and management of data regarding the unspecific behaviors of special-needs students are needed. In this paper, a consultation management model based on behavior classification of special-needs students using machine learning is proposed. It collects data by photographing the behavior of special students in real time, analyzes the behavior pattern, composes a data set, and trains it in the suggestion system. It is possible to improve the accuracy by comparing the behavior of special students photographed later into the suggestion system and analyzing the results by comparing it with the existing data again. The test has been performed by arbitrarily applying unspecific behaviors that are not stored in the database, and the forecast model has accurately classified and grouped the input data. Also, it has been verified that it is possible to accurately distinguish and classify the behaviors through the feature data of the behaviors even if there are some errors in the input process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.695-712
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• 2021

As the use of smart devices is being increased rapidly by the development of internet and IT technology, the contents production and utilization rate are showing higher increase, too. In addition, the type of contents also shows very diverse forms such as education, game, video, UCC, etc. In the meantime, the contents are reproduced in diverse forms by reprocessing the original contents, and they are being serviced through the contents service platform. Therefore, the platform to make the contents reprocessing easy and fast is needed. As the diverse contents distribution channels such as YouTube, SNS, App Service, etc, easier contents distribution platform is needed, and the development of the relevant area is expected. In addition, as the selective consumption of the contents having easy accessibility through diverse smart devices is distinguished, the demand for the platform and service that can identify the contents consumption propensity by individual is being increased. Therefore, in this study, to vitalize the online contents distribution, the contents reproduction and publishing platform, was designed and materialized, which can reproduce and distribute the contents based on the real-time contents editing technology in URL unit and the consumer propensity analysis technology using the data management-based broadcasting contents distribution metadata technology and the edited image contents streaming technology. In addition, in the results of comparing with other platforms through the experiment, the performance superiority of the suggested platform was verified. If the suggested platform is applied to the areas of education, broadcasting, press, etc, the multi-media contents can be reproduced and distributed easily, through which the vitalization of contents-related industry is expected.

• Journal of the Korean Institute of Gas
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• v.25 no.3
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• pp.9-15
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• 2021

In this paper, a study on the construction of a knowledge base based on natural language processing and the design of a chemical substance estimation system for the development of a knowledge service for a real-time sensor information fusion detection system and symptoms of contact with chemical substances in industrial sites. The information on 499 chemical substances contact symptoms from the Wireless Information System for Emergency Responders(WISER) program provided by the National Institutes of Health(NIH) in the United States was used as a reference. AllegroGraph 7.0.1 was used, input triples are Cas No., Synonyms, Symptom, SMILES, InChl, and Formula. As a result of establishing the knowledge base, it was confirmed that 39 symptoms based on ammonia (CAS No: 7664-41-7) were the same as those of the WISER program. Through this, a method of establishing was proposed knowledge base for the symptom extraction process of the chemical substance estimation system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.1
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• pp.23-27
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• 2019

In this paper, we propose a micro vibro tomography(MVT) method, that can be used to visualize two-dimensional cross-sectional images and micro-vibration tomographic images in real time in a non-contact and non-destructive manner. The proposed method is based on the optical coherence tomography(OCT) technique, with an additionally customized image processing algorithm. The proposed method can detect the micro-motions or vibrations in sample structures by measuring the phase shift variations in the sample structures. In this study, we show the potential capabilities of the proposed MVT system for measuring the micro-vibrations generated when sound waves in a frequency range of 2~5 kHz are applied to an $80-{\mu}m$ thick latex phantom, which mimics the changes in physical structure of the human tympanic membrane while hearing. Additionally, three-dimensional volumetric images of the MVT method were recorded to observe the surface morphological changes in the surface of the phantom sample which mimics the human tympanic membrane while hearing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1544-1553
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• 2018

Recently internal information leakage in industries is severely increasing in spite of industry security policy. Thus, it is essential to prepare an information leakage prevention measure by industries. Most of the leaks result from the insiders, not from external attacks. In this paper, a real-time internal information leakage prevention system via both storage and network is implemented in order to protect confidential file leakage. In addition, a Hadoop-based user behavior analysis and statistics system is designed and implemented for storing and analyzing information log data in industries. The proposed system stores a large volume of data in HDFS and improves data processing capability using RHive, consequently helps the administrator recognize and prepare the confidential file leak trials. The implemented audit system would be contributed to reducing the damage caused by leakage of confidential files inside of the industries via both portable data media and networks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.161-167
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• 2019

Recently, demand forecasting techniques have been actively studied due to interest in stable power supply with surging power demand, and increase in spread of smart meters that enable real-time power measurement. In this study, we proceeded the deep learning prediction model experiments which learns actual measured power usage data of home and outputs the forecasting result. And we proceeded pre-processing with moving average method. The predicted value made by the model is evaluated with the actual measured data. Through this forecasting, it is possible to lower the power supply reserve ratio and reduce the waste of the unused power. In this paper, we conducted experiments on three types of networks: Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) and we evaluate the results of each scheme. Evaluation is conducted with following method: MSE(Mean Squared Error) method and MAE(Mean Absolute Error).

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.120-126
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• 2019

Recently, there is an increasing need for highly precise processing with the rapid development of precision machinery, electrical and electronics, and semiconductor industries. Cutting machine control relies on the operator's sense and experience in tradition, but it has been greatly enhanced by the adoption of CNC(Computerized Numeric Controller). In addition, cutting dynamics technology has been paid attention to reflect the operating state of machine in real time. This paper presents a method to detect and stabilize tool vibration by attaching an acoustic sensor to a CNC machine. The sensed acoustic data is synchronized with the tool position and the abnormal vibration frequency is separated from the collected acoustic frequency, then analyzed to detect the tool vibration. Also the reliability the tool vibration detection and stabilization is improved by applying the cutting dynamic method. The proposed method is analyzed and evaluated in terms of the surface roughness.