• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.565-571
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• 2021

In this paper, a compensation scheme of frequency selective IQ mismatch for high-performance radar systems based on commercial RFIC's is proposed. Besides, an optimization model and its solution based on the dimension reduction scheme using singular value decomposition are also proposed to design the optimal IQ mismatch compensation digital filter with complex coefficients. The performance of the proposed method had been analyzed through experiments using the IQ mismatch measurement and compensation system implemented on an FPGA board with a target RFIC and compared with the previous method. The experiment result showed a performance improvement of the proposed method over the existing one without noticeable increments in complexities. These performance analysis results showed that the limitation of using commercial RFIC's in high-performance radar systems due to the undesirable maximum SNR cap caused by their IQ mismatches could be overcome by employing the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1942-1948
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• 2021

The cadastre in Korea was established with the outdated technology of the Japanese colonial period, and thus currently 15% of the Korea domestic land does not match the cadastral map. Accordingly, the government has been establishing the Korean cadastre under the name of 'Cadastral Re-investigation Project' and is changing the origin of the survey to the world geodetic system. Assuming that the project is completed, we propose a drone boundary survey method that can be used to easily survey using the exact digital cadastral information. The developed mobile GCS application can control the drone and acquire the boundary point coordinates recorded in the cadastre, and the drone automatically flies to mark the boundary points. The developed prototype of drone made a tour along the 6 boundary points in 2 minutes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1525-1530
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• 2022

BGP(Border Gateway Protocol) is a routing protocol that is actively used in inter-AS routing on the Internet. However, BGP routing protocol is vulnerable to BGP hijacking attacks that hijack the network by impersonating normal BGP sessions. BGP Hijacking attacks can lead to causing intercept IP traffic or interference with the normal service operation. Recently, BGP hijacking attacks, which have often occurred overseas, have also occurred in Korea. It means threatening the security of the Internet. In this paper, we analyze the overall process of attack through representative attack cases and virtual scenarios of BGP hijacking and based on the results of analyzing the application status of security technology to prevent BGP hijacking attacks by Korea and global major ISPs. It covers the technical proposal of ISPs and autonomous system operators should take to defend against BGP hijacking attacks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.656-661
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• 2022

Password with a combination of 4-digit numbers has been widely adopted for various authentication systems (such as credit card authentication, digital door lock systems and so on). However, this system could not be safe because the 4-digit password can easily be stolen by predicting it from the fingermarks on the keypad or display screen. Furthermore, due to the prolonged COVID-19 pandemic, contactless method has been preferred over contact method in authentication. This paper suggests a new password input method based on eyeblink pattern analysis in video sequence. In the proposed method, when someone stands in front of a camera, the sequence of eyeblink motions is captured (according to counting signal from 0 to 9 or 9 to 0), analyzed and encoded, producing the desired 4-digit decimal numbers. One does not need to touch something like keypad or perform an exaggerated action, which can become a very important clue for intruders to predict the password.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.83-94
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• 2023

The study examines the current status of introducing technological innovation in the Uzbekistan logistics industry and proposes opportunities and challenging factors for the logistics industry following the introduction of technological innovation in the future. The analysis results revealed that the information technology used in the Uzbekistan logistics industry following technologies: RFID, barcode, Cool Guardian, tracking system, transport satellite monitoring, digital TIR, and GPS monitoring. In addition, Uzbekistan has recently been increasing investment in advanced information and communication technology not only at the corporate level but also at the governmental level in anticipation of becoming a hub for logistics. Based on these analysis results, the Uzbekistan logistics industry's proposed opportunities and challenging factors can be used as basic information for government policymakers, transportation and logistics companies, and various partners. It can also be used by logistic companies that seek to take advantage of Uzbekistan's strategic location to create a logistics hub in Central Asia.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.445-452
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• 2022

Class distribution of unbalanced data is an important part of the digital world and is a significant part of cybersecurity. Abnormal activity of unbalanced data should be found and problems solved. Although a system capable of tracking patterns in all transactions is needed, machine learning with disproportionate data, which typically has abnormal patterns, can ignore and degrade performance for minority layers, and predictive models can be inaccurately biased. In this paper, we predict target variables and improve accuracy by combining estimates using Synthetic Minority Oversampling Technique (SMOTE) and Light GBM algorithms as an approach to address unbalanced datasets. Experimental results were compared with logistic regression, decision tree, KNN, Random Forest, and XGBoost algorithms. The performance was similar in accuracy and reproduction rate, but in precision, two algorithms performed at Random Forest 80.76% and Light GBM 97.16%, and in F1-score, Random Forest 84.67% and Light GBM 91.96%. As a result of this experiment, it was confirmed that Light GBM's performance was similar without deviation or improved by up to 16% compared to five algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.181-186
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• 2022

Poisson-Boltzmann equation (PBE) is used to model problems arising from various disciplinary including bio-pysics and colloid chemistry. Therefore, to predict a numerical solution of PBE is an important issue. The authors proposed deep learning based methods to solve PBE while the computational time to generate finite element method (FEM) solutions were bottlenecks of the algorithms. In this work, we shorten the generation time of FEM solutions in two directions. First, we experimentally find certain penalty parameter in a bilinear form. Second, we applied algebraic multigrids methods to the algebraic system so that condition number is bounded regardless of the meshsize. In conclusion, we have reduced computation times to solve algebraic systems for PBE. We expect that algebraic multigrids methods can be further employed in various disciplinary to generate deep learning samples.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1846-1852
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• 2021

In modern society, various digital devices are being distributed in a wide range of fields due to the fourth industrial revolution and the development of IoT technology. However, noise is generated in the process of acquiring or transmitting an image, and not only damages the information, but also affects the system, causing errors and incorrect operation. AWGN is a representative noise among image noise. As a method for removing noise, prior research has been conducted, and among them, AF, A-TMF, and MF are the representative methods. Existing filters have a disadvantage that smoothing occurs in areas with high frequency components because it is difficult to consider the characteristics of images. Therefore, the proposed algorithm calculates the standard deviation distribution to effectively eliminate noise even in the high frequency domain, and then calculates the final output by applying the probability density function weight of the Laplace distribution using the curve fitting method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.213-215
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• 2021

With the development of IoT technology, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. In particular, a system based on a digital image may cause a malfunction due to noise in the image due to a sensor defect or a communication environment problem. Therefore, research on image processing has been continued as a pre-processing process, and an effective noise reduction technique is required depending on the type of noise and the characteristics of the image. In this paper, we propose a modified spatial weight filter to protect edge components in the impulse noise reduction process. The proposed algorithm divides the filtering mask into four regions and calculates the standard deviation of each region. The final output was filtered by applying a spatial weight to the region with the lowest standard deviation value. Simulation was conducted to evaluate the performance of the proposed algorithm, and it showed superior impulse noise reduction performance compared to the existing method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.186-197
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• 2023

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy's status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of 'AIR_TEMPERATURE' data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real-world scenarios.