• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.117-125
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• 2024

In this paper, implement and propose an Active Electronically Scanned Array (AESA) radar integration analysis system which specialized for radar development by using heterogeneous media. Most analysis systems are used to analyze and improve the cause of defects, so they help the test easier. However, previous log analysis systems that operate only based on text are not intuitive and difficult to find the information user want at once if there is a lot of log information. so when an equipment defect occurs, there are limitations in analyzing the cause of defect. Therefore, the analysis system in this paper utilizes heterogeneous media. The media defined in this paper refers to recording text-based data, displaying data as image or video and visualizing data. The proposed analysis system classifies and stores data that transmitted and received between radar devices, radar target detection and Tracking algorithm data, etc. also displays and visualizes radar operation results and equipment defect information in real time. With this analysis system, it can quickly provide information what user want and assistance in developing high quality radar.

• Smart Structures and Systems
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• v.33 no.4
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.25-37
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• 2024

This study proposes a novel automation system that integrates Optical Character Recognition (OCR) and Retrieval-Augmented Generation (RAG) technologies to enhance the efficiency of the ESG (Environmental, Social, and Governance) document review process. The proposed system improves text recognition accuracy by applying an ensemble model-based image preprocessing algorithm and hybrid information extraction models in the OCR process. Additionally, the RAG pipeline optimizes information retrieval and answer generation reliability through the implementation of layout analysis algorithms, re-ranking algorithms, and ensemble retrievers. The system's performance was evaluated using certificate images from online portals and corporate internal regulations obtained from various sources, such as the company's websites. The results demonstrated an accuracy of 93.8% for certification reviews and 92.2% for company regulations reviews, indicating that the proposed system effectively supports human evaluators in the ESG assessment process.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.149-156
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• 2024

This paper implemented a drill bit foreign matter inspection system based on the YOLO V3 algorithm and evaluated its performance. The study trained the YOLO V3 model using 600 training data to distinguish between the normal and foreign matter states of the drill bit. The implemented inspection system accurately analyzed the state of the drill bit and effectively detected defects through automatic inspection. The performance evaluation was performed on drill bits used more than 2,000 times, and achieved a recognition rate of 98% for determining whether resharpening was possible. The goal of foreign matter removal in the cleaning process was evaluated as 99.6%, and the automatic inspection system could inspect more than 500 drill bits per hour, which was about 4.3 times faster than the existing manual inspection method and recorded a high accuracy of 99%. These results show that the automated inspection system can dramatically improve inspection speed and accuracy, and can contribute to quality improvement and cost reduction in manufacturing sites. In future studies, it is necessary to develop more efficient and reliable inspection technology through system optimization and performance improvement.

• The Transactions of the Korea Information Processing Society
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• v.13 no.10
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• pp.568-573
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• 2024

Accurate placement of prosthetics represents a critical aspect of dental implant surgery. however, it heavily depends on the dentist's decision and visual obstructions caused by various factors can lead to errors during the procedure. This paper proposes a 2D image-based real-time implant placement guiding system that predicts the implant position using 2D surgical video without the need for preoperative oral scans or 3D model generation. In the initial phase of the surgical video, two segmentation models are employed to measure prior statistics of the occlusal and incisal surfaces for each tooth. Subsequently, a single segmentation model is used to separate the occlusal and incisal surfaces, and the implant placement is predicted and guided based on the axis and length of adjacent teeth as well as the center of the prosthesis to be implanted. The system was designed and implemented using a dental phantom model, which replicates the oral structure of an actual human. The algorithm's average execution time for guiding implant placement on 10 images was measured to be 12.14 ms, demonstrating its feasibility for real-time application in surgical video.

• The Journal of Information Systems
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• v.33 no.3
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• pp.187-205
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• 2024

Purpose A prediction model for occupational injuries can support more proactive, efficient, and effective policy-making. This study aims to develop a model that predicts the severity of occupational injuries, classified into 15 disability grades in South Korea, using machine learning techniques applied to COMWEL data. The primary goal is to improve prediction accuracy, offering an advanced tool for early intervention and evidence-based policy implementation. Design/methodology/approach The data analyzed in this study consists of 290,157 administrative records of occupational injury cases collected between 2018 and 2020 by the Korea Workers' Compensation & Welfare Service, based on the 'Workers' Compensation Insurance Application Form' submitted for occupational injury treatment. Four machine learning models - Decision Tree, DNN, XGBoost, and LightGBM - were developed and their performances compared to identify the optimal model. Additionally, the Permutation Feature Importance (PFI) method was used to assess the relative contribution of each variable to the model's performance, helping to identify key variables. Findings The DNN algorithm achieved the lowest Mean Absolute Error (MAE) of 0.7276. Key variables for predicting disability grades included the severity index, primary disease code, primary disease site, age at the time of the injury, and industry type. These findings highlight the importance of early policy intervention and emphasize the role of both medical and socioeconomic factors in model predictions. The academic and policy implications of these results were also discussed.

• Progress in Medical Physics
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• v.21 no.3
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• pp.304-310
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• 2010

Less execution of the electron arc treatment could in large part be attributed to the lack of an adequate planning system. Unlike most linear accelerators providing the electron arc mode, no commercial planning systems for the electron arc plan are available at this time. In this work, with the expectation that an easily accessible planning system could promote electron arc therapy, a commercial planning system was commissioned and evaluated for the electron arc plan. For the electron arc plan with use of a Varian 21-EX, Pinnacle3 (ver. 7.4f), with an electron pencil beam algorithm, was commissioned in which the arc consisted of multiple static fields with a fixed beam opening. Film dosimetry and point measurements were executed for the evaluation of the computation. Beam modeling was not satisfactory with the calculation of lateral profiles. Contrary to good agreement within 1% of the calculated and measured depth profiles, the calculated lateral profiles showed underestimation compared with measurements, such that the distance-to-agreement (DTA) was 5.1 mm at a 50% dose level for 6 MeV and 6.7 mm for 12 MeV with similar results for the measured depths. Point and film measurements for the humanoid phantom revealed that the delivered dose was more than the calculation by approximately 10%. The electron arc plan, based on the pencil beam algorithm, provides qualitative information for the dose distribution. Dose verification before the treatment should be mandatory.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.51-63
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• 2000

Dynamic traffic assignment(DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvement, especially regarding its formulation and solution algerian Recently, with its promise for In(Intelligent Transportation System) and GIS(Geographic Information System) applications, DTA have received increasing attention. This potential also implies higher requirement for DTA modeling, especially regarding its solution efficiency for real-time implementation. But DTA have many mathematical difficulties in searching process due to the complexity of spatial and temporal variables. Although many solution algorithms have been studied, conventional methods cannot iud the solution in case that objective function or constraints is not convex. In this paper, the genetic algorithm to find the solution of DTA is applied and the Merchant-Nemhauser model is used as DTA model because it has a nonconvex constraint set. To handle the nonconvex constraint set the GENOCOP III system which is a kind of the genetic algorithm is used in this study. Results for the sample network have been compared with the results of conventional method.

• Korean Journal of Optics and Photonics
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• v.16 no.3
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• pp.182-190
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• 2005

The fiber Raman amplifier(FRA) is a distinctly advantageous technology. Due to its wider, flexible gain bandwidth, and intrinsically lower noise characteristics, FRA has become an indispensable technology of today. Various FRA modeling methods, with different levels of convergence speed and accuracy, have been proposed in order to gain valuable insights for the FRA dynamics and optimum design before real implementation. Still, all these approaches share the common platform of coupled ordinary differential equations(ODE) for the Raman equation set that must be solved along the long length of fiber propagation axis. The ODE platform has classically set the bar for achievable convergence speed, resulting exhaustive calculation efforts. In this work, we propose an alternative, highly efficient framework for FRA analysis. In treating the Raman gain as the perturbation factor in an adiabatic process, we achieved implementation of the algorithm by deriving a recursive relation for the integrals of power inside fiber with the effective length and by constructing a matrix formalism for the solution of the given FRA problem. Finally, by adiabatically turning on the Raman process in the fiber as increasing the order of iterations, the FRA solution can be obtained along the iteration axis for the whole length of fiber rather than along the fiber propagation axis, enabling faster convergence speed, at the equivalent accuracy achievable with the methods based on coupled ODEs. Performance comparison in all co-, counter-, bi-directionally pumped multi-channel FRA shows more than 102 times faster with the convergence speed of the Average power method at the same level of accuracy(relative deviation < 0.03dB).

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.