• 한국컴퓨터정보학회논문지
• /
• 제26권1호
• /
• pp.155-161
• /
• 2021

최근 UAV를 활용하는 정밀 추적이나 임무완수 등의 군사 목적의 연구가 활발하게 진행되고 있다. 특히 앞서가는 유도 UAV의 자세 정보를 추정하고 이 정보를 이용하여 임무 UAV가 스텔스로 따라가서 자신의 임무를 완수하는 기능이 필요한 경우에는 유도 UAV의 자세 정보 추정 속도를 실시간으로 처리 해야만 한다. 최근까지 영상처리와 칼만 필터를 사용해서 앞서가는 유도 UAV의 자세정보를 정밀하게 추정하는 연구가 수행되어 왔으나 처리과정의 순차처리로 인해 처리속도에 있어 문제점이 있어왔다. 따라서 본 연구에서는 영상 처리에 있어 처리영역을 전체영역이 아닌 물체를 포함하는 ROI 영역으로 한정하고 또한 연속적인 처리 과정을 OpenMP 기반의 멀티스레드로 분배하고 스레드동기를 맞추어서 병렬 형태로 처리함으로써 자세정보 추정 속도를 향상시킬 수 있는 방법을 제안한다. 구현 결과를 통해서 기본의 처리에 비해 45%이상 처리 속도를 향상시킴으로써 실시간처리가 가능하게 되어 임무 UAV의 추적 기능 향상을 통한 임무 완수 가능성을 증가시킬 수 있음을 확인하였다.

• International Journal of Computer Science & Network Security
• /
• 제22권4호
• /
• pp.193-202
• /
• 2022

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.

• 인간식물환경학회지
• /
• 제24권6호
• /
• pp.563-572
• /
• 2021

Background and objective: The ongoing COVID-19 pandemic restricted daily life, forcing people to spend time indoors. With the growing interest in mental health issues and residential environments, 'pet plants' have been receiving attention during the unprecedented social distancing measures. This study aims to analyze the change in trends of pet plants before and during the COVID-19 pandemic and provide basic data for studies related to pet plants and directions of future development. Methods: A total of 2,016 news articles using the keyword 'pet plants' were collected on Naver News from January 1, 2018 to August 15, 2019 (609 articles) and January 1, 2020 to August 15, 2021 (1,407 articles). The texts were tokenized into words using KoNLPy package, ultimately coming up with 63,597 words. The analyses included frequency of keywords and topic modeling based on Latent Dirichlet Allocation (LDA) to identify the inherent meanings of related words and each topic. Results: Topic modeling generated three topics in each period (before and during the COVID-19), and the results showed that pet plants in daily life have become the object of 'emotional support' and 'healing' during social distancing. In particular, pet plants, which had been distributed as a solution to prevent solitary deaths and depression among seniors living alone, are now expanded to help resolve the social isolation of the general public suffering from COVID-19. The new term 'plant butler' became a new trend, and there was a change in the trend in which people shared their hobbies and information about pet plants and communicated with others in online. Conclusion: Based on these findings, the trend data of pet plants before and after the outbreak of COVID-19 can provide the basis for activating research on pet plants and setting the direction for development of related industries considering the continuous popularity and trend of indoor gardening and green hobby.

• 한국지리정보학회지
• /
• 제25권4호
• /
• pp.151-162
• /
• 2022

최근 우리나라의 산림은 지속적인 산림재해로 인해 피해가 누적되고 있어 산림을 관리하기 위한 모니터링 기술이 조명받고 있으며, 산림재해 피해대상지의 규모가 큰 지형 특성으로 인해 드론, 인공지능, 빅데이터 등을 활용한 기술들이 연구되고 있다. 본 연구에서는 산림재해의 병해충을 모니터링하기 위해 딥러닝과 드론을 활용하여 산림 병해충 피해 의심목을 자동으로 탐지하는 산림 병해충 자동탐지 알고리즘 개발을 위한 표준 데이터 세트를 구축하였다. 객체검출 알고리즘으로서 YOLO 알고리즘을 활용한 실험결과에서는 YOLOv4-P7 모델이 재현율 69.69%와 정밀도 69.15%로 가장 높게 나타났으며, 이미지 사이즈가 큰 정사영상인 검출대상임을 고려할 때 산림병해충 피해의심목 자동탐지 알고리즘으로 YOLOv4-P7이 적합함을 확인하였다.

• 디지털정책학회지
• /
• 제1권2호
• /
• pp.17-24
• /
• 2022

중국에서는 코드 스캔 결제의 지속적인 홍보 및 적용으로 인해 코인 시장의 불균형이 발생되다. 동전 교환기는 이 문제를 완화할 수 있을 뿐만 아니라 상업 은행의 비즈니스 발생에도 적극적인 영향을 미친다. 따라서 동전 교환기를 연구하는 것은 매우 중요하다. 본 연극이 연극목적은 동전 교환기가 중국 상업 은행의 사업에 미치는 영향을 연구하는 것이다. 현장 방문을 통해 수집한 중국 상업 은행의 고객 데이터를 재무 지표 계산 방법과 결합하여 사례 분석을 수행한다. 연구 결과에 따르면 동전 교환기는 중국 상업 은행의 비즈니스 발전에 긍정적인 영향을 미친다. 본 연극는 중국 상업 은행에 대한 타당성 제안 및 비즈니스 개발에 대한 새로운 아이디어를 제공한다. 현재 동전교환기에 대한 연구는 거의 없으며, 본 연구는 재정지표 계산을 결합하여 정책성과를 검증하는 것이 본 연구의 혁신점이다.

• 한국산업융합학회 논문집
• /
• 제25권6_2호
• /
• pp.1119-1125
• /
• 2022

The depletion of resources and waste disposal caused by the continuous development of industry have emphasized the need to reduce consumption and production, recycle and reuse, and the importance of remanufacturing has increased in recent years. The spindle part of the aging planner miller, which is currently being remanufactured, is one of the factors that has the greatest impact on the performance of the machine tool. When designing the spindle part of the spindle shaft, there are considerations such as the configuration size bearing performance of the main shaft, but the diameter of the main shaft, the dangerous speed bearing, and the arrangement that affect the machining accuracy should be basically considered. As such, various studies have been conducted on the design of machine tool spindle spindles, but research on the reverse engineering of existing aging machine tool spindle spindles is poor. Reverse engineering is designing in the direction of improving performance by extracting specifications from already finished products, and first scanning the reverse engineered object through a 3D scanner, 3D modeling is performed based on the collected data, and then the process of deriving improvement plans by reverberating to improve performance by identifying wear and damage conditions is followed. Therefore, in this study, the purpose of this study is to provide data on reverse engineering by deriving improvement plans through optimal design for the bearing position of the aging planar Miller spindle spindle using central composite programming.

• 한국산업융합학회 논문집
• /
• 제25권6_2호
• /
• pp.1097-1102
• /
• 2022

With the continuous development of the current industry, the current global environment is in a very serious situation, with resource supply and demand dependent on imports and huge costs for waste disposal due to the depletion of resources and mass generation of industrial waste. Its limitations have already been revealed in many fields, and the importance of re-manufacturing is drawing attention as a countermeasure to these problems. Re-manufacturing aims to recover products that are in the aging and disposal stages, recover to performance close to new products, and re-commercialize them. Among them, most of the machine tools are made of materials such as steel and cast iron with large structures, and raw materials are widely used when producing new products. In addition, since a lot of carbon is generated due to production, it is an object that can obtain a great re-manufacturing effect. Planner millers belonging to large machine tools are one of the machine tool equipment that can greatly reduce resources and energy through re-manufacturing because the structure is very large and the casting is several to tens of tons. Through this machine tool, performance tests and results are derived on the development of re-manufacturing source technology and domestic servo motor and CNC control device.

• 천문학회보
• /
• 제45권1호
• /
• pp.42.2-42.2
• /
• 2020

We report on multi-wavelength observations of AA Ori, a Young Stellar Object in Orion-A star-forming region. AA Ori is known to have a pre-transitional disk based on infrared observations including Spitzer/IRS data. We construct its broadband spectral energy distribution (SED) by not only taking data in the optical and IR region but also including Herschel/PACS, JCMT/SCUBA, and SMA observational data. We use the Monte Carlo radiative transfer code (RADMC-3D) to reconstruct the SED with a viscous accretion disk model initialized by a radially continuous disk and finally having an inner and outer dusty disk separated by a dust-depleted radial gap. By comparing the model SEDs with different configurations of disk parameters, we discuss the limits to find a single solution of model parameters to fit the data. We suggest that some models with a modified inner disk surface density gradient and some degree of dust depletion in the inner disk can explain the AA Ori's SED, from which we infer that the inner disk of AA Ori has evolved. We present that model configurations of a pre-transitional disk with a large gap extended to 60-80 AU in a settled dusty disk of a few hundred AU size with a high inclination angle (~60°) also create model SEDs close to the observed one. To distinguish whether the disk has a just-opened narrow gap or a large gap, with an altered surface density of the inner disk extended to 10 AU, we suggest a further investigation of AA Ori with high angular resolution observations.

• 천문학회보
• /
• 제45권1호
• /
• pp.46.3-47
• /
• 2020

Variations in the brightness of asteroids are caused by their spins, irregular shapes and companions. Thus, in principle, the spin state and shape model of a single object or, a combined model of spins, shapes and mutual orbit of a multiple components can be constructed from the analysis of light curves obtained from the time-series photometry. Using ground- and space-based facilities, a number of time-series photometric observations of asteroids have been conducted to find the possible causes of their light variations. Nonetheless, only about 2% of the known asteroids have been confirmed for their rotation periods. Therefore, a follow-on systematic photometric survey of asteroids is essential. We started an asteroid light curve survey for this purpose using Korea Microlensing Telescope Network (KMTNet) during 199 nights between the second half of 2019 and the first half of 2020. We monitored within a 2° × 14° region of the sky per each night with 25 min cadences. In order to observe as many asteroids as possible with a single exposure, we mostly focus on the ecliptic plane. In our survey, 25,925 asteroids were observed and about 8,000 of them were confirmed for their rotation periods. In addition, using KMTNet's 24-hour continuous monitoring, we collected many composite light curves of slow rotating asteroids that were rarely obtained with previous observations. In this presentation, we will introduce the typical light curves of asteroids obtained from our survey and present a statistical analysis of spin states and shapes of the asteroids from this study.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2022년도 추계학술대회
• /
• pp.239-241
• /
• 2022

현재 대한민국에서는 교통 혼잡을 해결하기 위해 다인승 전용차로 (HOV, High Occupancy Vehicle Lanes)와 지정차로제를 시행하고 있다. 현행의 교통단속 시스템은 단속 지역 구역에 인원이 필수로 배정되며 무인 단속에 어려움이 있다. 또한, 고정식 교통단속시스템은 속도 위반 단속은 가능하나 운전자가 네이게이션을 통해 단속을 회피할 수 있다. 이러한 문제점을 해결하기 위해 딥러닝 객체 인식 모델인 YOLO를 통한 교통 통합 단속 시스템이 필요하다. 본 연구에서는 멀티스레딩 기술 기반의 병렬처리 차량번호 인식 기술을 적용하여 불시 단속이 가능한 이동식 교통 통합 관리시스템을 제안한다. Yolo5를 이용한 차선 인식, 차량탑승인원 판별, 차량 번호 인식 등의 알고리즘을 통합 모델을 설계하고 이를 적용한 통합시스템을 제시하였다.