• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.221-236
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• 2022

BLE protocol prevents MITM attacks with user interaction through some input/output devices such as keyboard or display. Therefore, If it use a device which has no input/output facility, it can be vulnerable to MITM attack. If messages to be sent to a control device is forged by MITM attack, the device can be abnormally operated by malicious attack from attacker. Therefore, we describes a scenario which has the vulnerabilities of the BLE network in this paper and propose countermeasure method against MITM attacks integrity violations. Its mechanism provides data confidentiality and integrity with MD5 and security key distribution of Diffie Helman's method. In order to verify the effectiveness of the countermeasure method proposed in this paper, we have conducted the experiments. ​As experiments, the message was sent 200 times and all of them successfully detected whether there was MITM attack or not. In addition, it took at most about 4.2ms delay time with proposed countermeasure method between devices even attacking was going on. It is expected that more secure data transmission can be achieved between IoT devices on a BLE network through the method proposed.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.77-84
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• 2023

In autonomous robots, obstacle avoidance is a key feature. Potential Field is the most widely used method in this field. Such method requires real-time calculation of the nearest point of the obstacle from the robot, which involves difficulty of reliably segmenting the obstacle region from the distance sensor data profile. In this paper, Active Min-Depth Filter is introduced to obtain the nearest point of each obstacle using real-time calculation but without segmentation. Through simulations on various sensor noise environments, the robustness of the Active Min-Depth Filter could be confirmed, and successful results were obtained by applying real-world moving robots.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.98-107
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• 1995

The in-process detection of drill wear and breakage is one of the most importnat technical problems in unmaned machining system. In this paper, the monitoring system is developed to monitor abnormal drilling states such as drill breakage, drill wear and unstable cutting using motor current. Drill breakage is detected by level monitoring. Tool wear is classified by fuzzy pattern recognition. The key feature for classification of tool wear is the estimated flank wear which is calculated by the proposed flank wear model. The characteristic of the model is not sensitive to the variation of cutting conditions but is sensitive to drill wear state. Unstable cutting states due to the unsmooth chip disposal and the overload are monitored by the variance/mean ratio of spindle motor current. Variance/mean ratio also includes the information about the prediction of drill wear and drill breakage. The evaluation experiments have shown that the developed system works very well.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.2
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• pp.113-121
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• 2023

High-precision lasers and anti-aircraft radars are the main equipment to protect the Korean Peninsula, and require preemptive maintenance before signs of failure. Of the key components in the drive sector, bearings do not have a fault alarm function. Therefore, the technology for diagnosing defects in bearings before the performance degradation of equipment occurs is becoming more important. In this paper, for the experimental analysis, we measured the acceleration of the four sets of the same lot using acceleration sensors. Through periodic measurements, the factors that changed until the bearing stopped rotating were analyzed. To determine the replacement time, the main factors and threshold values of the bearing signal were analyzed. The error of the theoretical and experimental analysis results of the defect frequency was within 2.8 %, and the validity of the theoretical analysis results could be confirmed. Based on the results, it is possible to remotely transmit trouble alerts to users through the system check function.

• Journal of applied mathematics & informatics
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• v.41 no.1
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• pp.95-105
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• 2023

To make the network energy efficient and to protect the network from malignant user's energy efficient grid based secret key sharing scheme is proposed. The cost function is evaluated to select the optimal nodes for carrying out the data transaction process. The network is split into equal number of grids and each grid is placed with certain number of nodes. The node cost function is estimated for all the nodes present in the network. Once the optimal energy proficient nodes are selected then the data transaction process is carried out in a secured way using malicious nodes filtration process. Therefore, the message is transmitted in a secret sharing method to the end user and this process makes the network more efficient. The proposed work is evaluated in network simulated and the performance of the work are analysed in terms of energy, delay, packet delivery ratio, and false detection ratio. From the result, we observed that the work outperforms the other works and achieves better energy and reduced packet rate.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.37.1-37.1
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• 2021

For the first time in Korea, we are developing technology for gravitational wave (GW) detectors as a major R&D program. Our main research target is quantum noise reduction technology which can enhance the sensitivity of a GW detector beyond its limit by classical physics. Technology of generating squeezed vacuum state of light (SQZ) can suppress quantum noise (shot noise at higher frequencies and radiation pressure noise at lower frequencies) of laser interferometer type GW detectors. Squeezing technology has recently started being used for GW detectors and becoming necessary and key components. Our ultimate goal is to participate and make contribution to international collaborations for upgrade of existing GW detectors and construction of next generation GW detectors. This presentation will summarize our results in 2020 and plan for the upcoming years. Technical details will be presented in other family talks.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Tuberculosis and Respiratory Diseases
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• v.86 no.2
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• pp.94-101
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• 2023

A recent understanding of the dynamic continuous spectrum of Mycobacterium tuberculosis infection has led to the recognition of incipient tuberculosis, which refers to the latent infection state that has begun to progress to active tuberculosis. The importance of early detection of these individuals with a high-risk of progression to active tuberculosis is emphasized to efficiently implement targeted tuberculosis preventive therapy. However, the tuberculin skin test or interferon-γ release assay, which is currently used for the diagnosis of latent tuberculosis infection, does not aid in the prediction of the risk of progression to active tuberculosis. Thus, a novel test is urgently needed. Recently, simultaneous and systematic analysis of differentially expressed genes using a high-throughput platform has enabled the discovery of key genes that may serve potential biomarkers for the diagnosis or prognosis of diseases. This host transcriptional investigation has been extended to the field of tuberculosis, providing promising results. The present review focuses on recent progress and challenges in the field of blood transcriptional signatures to predict progression to active tuberculosis.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• The Journal of Bigdata
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• v.8 no.1
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• pp.35-47
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• 2023

It is crucial to develop effective and efficient big data analytics methods for problem-solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.