• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.10
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• pp.299-308
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• 2023

As the spectrum shortage problem has accelerated by the emergence of various services, New Radio-Unlicensed (NR-U) has appeared, allowing users who communicated in licensed bands to communicate in unlicensed bands. However, NR-U network users reduce the performance of Wi-Fi network users who communicate in the same unlicensed band. In this paper, we aim to simultaneously maximize the fairness and throughput of the unlicensed band, where the NR-U network users and the WiFi network users coexist. First, we propose an optimal power allocation scheme based on Monte Carlo Policy Gradient of reinforcement learning to maximize the sum of rates of NR-U networks utilizing rate-splitting multiple access in unlicensed bands. Then, we propose a channel occupancy time division algorithm based on sequential Raiffa bargaining solution of game theory that can simultaneously maximize system throughput and fairness for the coexistence of NR-U and WiFi networks in the same unlicensed band. Simulation results show that the rate splitting multiple access shows better performance than the conventional multiple access technology by comparing the sum-rate when the result value is finally converged under the same transmission power. In addition, we compare the data transfer amount and fairness of NR-U network users, WiFi network users, and total system, and prove that the channel occupancy time division algorithm based on sequential Raiffa bargaining solution of this paper satisfies throughput and fairness at the same time than other algorithms.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.11
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• pp.437-444
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• 2017

Electric Pole is a supporting beam used for power transmission/distribution which is sensitive to external force change of environmental factors. Therefore, power facilities have many difficulties in terms of maintenance/conservation from external environmental changes and natural disasters that cause a great economic impact. The aerial wire cause elasticity due to the influence of temperature, or factors such as wind speed and wind direction, that weakens the electric pole. The situation may lead to many safety risk in day-to-day life. But, the safety assessment of the pole is carried out at the design stage, and aftermath is not considered. For the safety and maintenance purposes, it is very important to analyze the influence of weather factors on external forces periodically. In this paper, we analyze the acceleration data of the sensor nodes installed in electric pole for maintenance/safety purpose and use Kalman filter as noise compensation method. Fast Fourier Transform (FFT) is performed to analyze the influence of each meteorological factor, along with the meteorological factors on frequency components. The result of the analysis shows that the temperature, humidity, solar radiation, hour of daylight, air pressure, wind direction and wind speed were influential factors. In this paper, the influences of meteorological factors on frequency components are different, and it is thought that it can be an important factor in achieving the purpose of safety and maintenance.

• Journal of Satellite, Information and Communications
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• v.9 no.3
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• pp.27-32
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• 2014

A doppler shift is generated by moving a transmitter or receiver operated in communication systems. The doppler frequency shift between a transmitter and a receiver or the frequency offset present in transceivers must be removed to get the wanted system performance. FTS is used for preventing an accident from operating abnormally and for guaranteeing public protection. A launching vehicle's initial velocity is very fast in order to escape the earth and the amount of doppler shift is large. Recently many studies to adopt the next generation FTS are ongoing. To introduce new FTS, the effects of doppler shift on the performance of the new FTS must be studied. In this paper the doppler effect caused by launching vehicle's velocity affecting the performance of FTS receiver is investigated into two cases, one is for EFTS as a digital FTS and the other is for FTS using a tone signal. Noncoherent DPSK and noncoherent CPFSK are considered as the modulation methods of EFTS. In the cases of the doppler frequency shift of 200Hz present in EFTS using noncoherent DPSK and noncoherent CPFSK are simulated. Simulation results show that $E_b/N_o$ of 0.5dB deteriorates in the region of near BER of about $10^{-5}$ in RS coding. And there is no performance variation in $E_b/N_o$ or $E_b/N_o$ is worsened about 0.1dB in the same BER region for the case of using convolutional and BCH coding. Quadrature detector used in FTS using tone signals is not influenced by the doppler frequency shift.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.10
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• pp.709-721
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• 2006

The traditional broadcasting services over terrestrial, satellite and cable media have been unidirectional mass media regardless of TV viewer's preferences. Recently ich media streaming has become possible via the broadb and networks. Furthermore, since bidirectional communication is possible, personalcasting such as personalized streaming services has been emerging by taking into account the user's preference on content genres, viewing times and actors/actresses etc. Accordingly, personal media becomes an important means for content provision service in addition to the traditional broadcasting service as mass media. In this paper, we introduce a user profile reasoning method for target advertisement which is considered an important application in personalcasting service. The proposed user profile reasoning method predicts an unknown TV viewer's gender and ages by analyzing TV Viewing history data. Based on the estimated user's gender and ages, a target advertisement is provided with TV Anytime metadata. A proposed target advertisement system is developed based on the user profile reasoning and the target advertisement selection method. To show the effectiveness of our proposed methods, we present a plenty of experimental results by using realistic TV viewing history data.

• Communications of Mathematical Education
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• v.34 no.1
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• pp.1-15
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• 2020

Today's healthcare, intelligent robots, smart home systems, and car sharing are already innovating with cutting-edge information and communication technologies such as Artificial Intelligence (AI), the Internet of Things, the Internet of Intelligent Things, and Big data. It is deeply affecting our lives. In the factory, robots have been working for humans more than several decades (FA, OA), AI doctors are also working in hospitals (Dr. Watson), AI speakers (Giga Genie) and AI assistants (Siri, Bixby, Google Assistant) are working to improve Natural Language Process. Now, in order to understand AI, knowledge of mathematics becomes essential, not a choice. Thus, mathematicians have been given a role in explaining such mathematics that make these things possible behind AI. Therefore, the authors wrote a textbook 'Basic Mathematics for Artificial Intelligence' by arranging the mathematics concepts and tools needed to understand AI and machine learning in one or two semesters, and organized lectures for undergraduate and graduate students of various majors to explore careers in artificial intelligence. In this paper, we share our experience of conducting this class with the full contents in http://matrix.skku.ac.kr/math4ai/.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.7
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• pp.303-310
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• 2013

As the role of software increases in computing environments, issues in software security become more important problems. Dynamic taint analysis is a technique to trace and manage tainted data originated from unreliable sources during the execution of a program. This analysis can be applied to software security verification as well as software behavior understanding, testing unexpected errors, or debugging. In the previous researches, they focussed only to show the analysis results of dynamic taint analysis, and they did not logically describe propagation process of tainted data and analysis procedures. So, there were difficulties in understanding the analysis procedures or applying to other analysis. In this paper, by theoretically describing the analysis procedure, we logically show how the propagation process of tainted data can be traced, and present a theoretical model for dynamic taint analysis. In addition, we verify the correctness of the proposed model by implementing an analyser, and show that propagation of tainted data can be traced by the model. The proposed model can be applied to understand the analysis procedures of data flows in dynamic taint analysis, and can be used as an base knowledge for designing and implementing analysis method, which applies such analysis method.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.241-247
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• 2016

As a domestic traffic control signal system, either the system with which a traffic signal turns into green at regular intervals or the system with which an amber or a red signal flickers on local roads without heavy traffic at midnight has been utilized. However, when the former system is used for roads with light traffic at midnight, delays and congestion can be incurred. Besides, in case of the latter signal system, the risk of vehicle crash is high. This study proposes a response type of flickering green signal system that rearranges signal system after analyzing beacon messages including sensor data. The proposed system, on a trunk road or a branch road at midnight, makes the signal keep flickering in green; When a vehicle enters the range of RSE, the transfer coverage, it transmits beacon messages regularly and Agent System analyzes the messages and alters the signal. It is a system by which vehicles move following the altered signal system, which will not only ensure smooth flow but also prevent vehicles from crashing on a road with light traffic. As a result of a simulation, traffic throughput and the average waiting time displayed 10 to 30 percent better improvement than existing signal systems, in terms of performance.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.7
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• pp.32-37
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• 2008

Satellite communication systems including the DVB-S2 (Digital Video Broadcasting - Satellite Version 2) system require operations under low signal-to-noise ratio (SNR) and large frequency offset values, and the initial frame synchronization process necessitates a robust correlation method. While a variety of conventional correlation structures exist for the initial synchronization, each method has different characteristics and performance in different channel environments. In this paper, we propose new correlation methods which exhibit enhanced performance in low SNR and large frequency offsets, and analyze their performance. The proposed methods use the magnitude sum and vector sum of extended differential correlation values, to maximize the correlation between the received signal and the synchronization sequence by using the spanned differential correlation result. The magnitude sum correlation method has better performance compared to conventional methods including the approximated ML (Maximum likelihood) method for SNR values below 4 dB with or without frequency offsets. The vector sum correlation method has improved performance over the magnitude sum method for channels with relatively small frequency offsets.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.6
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• pp.135-142
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• 2016

We proposed the new method to estimate the blood pressure with the differential value of the digital arterial pulse waveform and BP relation equation. To get the digital arterial pulse waveform, we use the arterial pulse waveform measurement system that has digital air-pressure sensor device and smart phone. The acquired digital arterial pulse waveforms are classified as hypertension group, normal group, and hypotension group, and we can derive the average differential value between the highest point and lowest point of a single waveform of individuals along with the group. In this study, we found the functional correlation between the blood pressure and differential value as a form of BP relation equation through the regression process on the average of differential value and blood pressure value from a tonometer. The Experimental results show the BP relation equation can give easy blood pressure estimation method with a high accuracy. Although this estimation method has over 66 % error rate and does not give the high level of the accuracy for the diastolic compares to the commercial tonometer, the estimation results for the systolic show the high accuracy that has less than 10 % error rate.