• Title/Summary/Keyword: 최신 기술

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Deep Learning based Fish Object Detection and Tracking for Smart Aqua Farm (스마트 양식을 위한 딥러닝 기반 어류 검출 및 이동경로 추적)

  • Shin, Younghak;Choi, Jeong Hyeon;Choi, Han Suk
    • The Journal of the Korea Contents Association
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    • v.21 no.1
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    • pp.552-560
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    • 2021
  • Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

Simplified Bridge Weigh-In-Motion Algorithm using Strain Response of Short Span RC T-beam Bridge with no Crossbeam installed (가로보가 없는 단지간 RC T빔교의 변형률 응답을 이용한 단순화된 BWIM (Bridge Weigh-In-Motion) 알고리즘)

  • Jeon, Jun-Chang;Hwang, Yoon Koog;Lee, Hee-Hyun
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.25 no.3
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    • pp.57-67
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    • 2021
  • A thorough administration of the arterial road network requires a continuous supply of updated and accurate information about the traffic that travels on the roads. One of the ways to effectively obtain the traffic volume and weight distribution of heavy vehicles is the BWIM technique, which is actively being studied. Unlike previous studies, this study was performed to develop a simplified Bridge Weigh-In-Motion (BWIM) algorithm that can easily estimate the axle spacing and weight of a traveling vehicle by utilizing the structural characteristics of the bridge. A short span RC T-beam bridge with no crossbeam installed was selected for the study, and then the strain response characteristics of bridge deck and girder was checked through preliminary field test. Based on the preliminary field test results, a simplified BWIM algorithm suitable for the bridge to be studied was derived. The validity and accuracy of the BWIM algorithm derived in this study were verified through field test. As a result of the verification test, the proposed BWIM algorithm can estimate the axle spacing and gross weight of the travelling vehicles with the average percent error of less than 3%.

Deep Learning Algorithm and Prediction Model Associated with Data Transmission of User-Participating Wearable Devices (사용자 참여형 웨어러블 디바이스 데이터 전송 연계 및 딥러닝 대사증후군 예측 모델)

  • Lee, Hyunsik;Lee, Woongjae;Jeong, Taikyeong
    • Journal of Korea Society of Industrial Information Systems
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    • v.25 no.6
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    • pp.33-45
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    • 2020
  • This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.

Roughness Analysis of Paved Road using Drone LiDAR and Images (드론 라이다와 영상에 의한 포장 노면의 평탄성 분석)

  • Jung, Kap Yong;Park, Joon Kyu
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.39 no.1
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    • pp.55-63
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    • 2021
  • The roughness of the road is an important factor directly connected to the ride comfort, and is an evaluation item for functional evaluation and pavement quality management of the road. In this study, data on the road surface were acquired using the latest 3D geospatial information construction technology of ground LiDAR, drone photogrammetry, and drone LiDAR, and the accuracy and roughness of each method were analyzed. As a result of the accuracy evaluation, the average accuracy of terrestrial LiDAR were 0.039m, 0.042m, 0.039m RMSE in X, Y, Z direction, and drone photogrammetry and drone LiDAR represent 0.072~0.076m, 0.060~0.068m RMSE, respectively. In addition, for the roughness analysis, the longitudinal and lateral slopes of the target section were extracted from the 3D geospatial information constructed by each method, and the design values were compared. As a result of roughness analysis, the ground LiDAR showed the same slope as the design value, and the drone photogrammetry and drone LiDAR showed a slight difference from the design value. Research is needed to improve the accuracy of drone photogrammetry and drone LiDAR in measurement fields such as road roughness analysis. If the usability through improved accuracy can be presented in the future, the time required for acquisition can be greatly reduced by utilizing drone photogrammetry and drone LiDAR, so it will be possible to improve related work efficiency.

Design and Implementation of Modbus Communications for Smart Factory PLC Data Collection (스마트팩토리 PLC 데이터 수집을 위한 Modbus 통신 설계 및 구현)

  • Han, Jin-Seok;Yoo, Jae-Soo
    • The Journal of the Korea Contents Association
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    • v.21 no.4
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    • pp.77-87
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    • 2021
  • Smart Factory refers to a factory that can be controlled by itself with an intelligent factory that improves productivity, quality and customer satisfaction by combining the entire process of manufacturing and production with digital automation solutions. The manufacturing industry around the world is rapidly changing, with Germany, Europe, and the United States at the center. In order to cope with such changes, the Korean government is also implementing a policy to spread the supply of smart factories for small and medium-sized companies, and related ministries and agencies such as the Ministry of Commerce, Industry and Energy, the Ministry of SMEs and Venture Business, the Korea Institute of Technology and Information Promotion, and local technoparks, as well as large companies such as Samsung, SK and LG are actively investing in smart manufacturing projects to support smart factories[1]. Factory Automation (FA) construction has many issues regarding the connection of heterogeneous equipment. The most difficult aspect of configuring various communications from various equipment is the reason. Although it may not be known if there are standards or products made up of the same company, it is not easy to build equipment that is old, up-to-date, and different use environments through a series of communications. To solve this problem, we would like to propose a method of communication using Modbus, one of FieldBus, which is one of the many industrial devices of PLC, a representative facility control system, and is used as a communication standard.

DECODE: A Novel Method of DEep CNN-based Object DEtection using Chirps Emission and Echo Signals in Indoor Environment (실내 환경에서 Chirp Emission과 Echo Signal을 이용한 심층신경망 기반 객체 감지 기법)

  • Nam, Hyunsoo;Jeong, Jongpil
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.3
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    • pp.59-66
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    • 2021
  • Humans mainly recognize surrounding objects using visual and auditory information among the five senses (sight, hearing, smell, touch, taste). Major research related to the latest object recognition mainly focuses on analysis using image sensor information. In this paper, after emitting various chirp audio signals into the observation space, collecting echoes through a 2-channel receiving sensor, converting them into spectral images, an object recognition experiment in 3D space was conducted using an image learning algorithm based on deep learning. Through this experiment, the experiment was conducted in a situation where there is noise and echo generated in a general indoor environment, not in the ideal condition of an anechoic room, and the object recognition through echo was able to estimate the position of the object with 83% accuracy. In addition, it was possible to obtain visual information through sound through learning of 3D sound by mapping the inference result to the observation space and the 3D sound spatial signal and outputting it as sound. This means that the use of various echo information along with image information is required for object recognition research, and it is thought that this technology can be used for augmented reality through 3D sound.

Evaluation of Regional Adaptability in Introduced Super Sweet Corn Hybrids and Heritability of Agronomic Traits (도입 초당옥수수 교잡종의 지역 적응성 및 농업 형질의 유전력 평가)

  • Lee, Shin-Young;Kang, Jong-won;Wang, Seung-hyun;Park, Tai-choon;Chung, Jong-Wook;So, Yoon-Sup
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.66 no.2
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    • pp.130-137
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    • 2021
  • This study evaluated newly introduced, commercial super sweet corn hybrids (Zea mays L.) for their potential as breeding materials. Agronomic traits were measured and variance components were obtained using a linear mixed model to estimate the heritability. The trials were carried out in 2018 at two locations (Haenam and Oksan in South Korea). All traits had low heritability, except for mid tasseling and silking days. These traits with low heritability mostly had low genetic variance component estimate. In case of ear height ratio, significant genotype by location appeared to be responsible for low genetic variance, which in turn led to low heritability. Low heritability estimates from the trials with commercial hybrids were perhaps because those hybrids were highly improved for commercial success. Hence, this does not necessarily point to them having poor potential as breeding materials. To overcome low heritability, significant genotype by environment interaction, and achieve high selection efficiency, intermating among hybrids is recommended to create new recombinants before inbred line development.

Long Range Forecast of Garlic Productivity over S. Korea Based on Genetic Algorithm and Global Climate Reanalysis Data (전지구 기후 재분석자료 및 인공지능을 활용한 남한의 마늘 생산량 장기예측)

  • Jo, Sera;Lee, Joonlee;Shim, Kyo Moon;Kim, Yong Seok;Hur, Jina;Kang, Mingu;Choi, Won Jun
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.23 no.4
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    • pp.391-404
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    • 2021
  • This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

Interface Establishment between Reinforcement Learning Algorithm and External Analysis Program for AI-based Automation of Bridge Design Process (AI기반 교량설계 프로세스 자동화를 위한 강화학습 알고리즘과 외부 해석프로그램 간 인터페이스 구축)

  • Kim, Minsu;Choi, Sanghyun
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.34 no.6
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    • pp.403-408
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    • 2021
  • Currently, in the design process of civil structures such as bridges, it is common to make final products by repeating the process of redesigning, if the initial design is found to not meet the standards after a structural review. This iterative process extends the design time, and causes inefficient consumption of engineering manpower, which should be put into higher-level design, on simple repetitive mechanical work. This problem can be resolved by automating the design process, but the external analysis program used in the design process has been the biggest obstacle to such automation. In this study, we constructed an AI-based automation system for the bridge design process, including an interface that could control both a reinforcement learning algorithm, and an external analysis program, to replace the repetitive tasks in the current design process. The prototype of the system built in this study was developed for a 2-span RC Rahmen bridge, which is one of the simplest bridge systems. In the future, it is expected that the developed interface system can be utilized as a basic technology for linking the latest AI with other types of bridge designs.

Cloud Detection from Sentinel-2 Images Using DeepLabV3+ and Swin Transformer Models (DeepLabV3+와 Swin Transformer 모델을 이용한 Sentinel-2 영상의 구름탐지)

  • Kang, Jonggu;Park, Ganghyun;Kim, Geunah;Youn, Youjeong;Choi, Soyeon;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.6_2
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    • pp.1743-1747
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    • 2022
  • Sentinel-2 can be used as proxy data for the Korean Compact Advanced Satellite 500-4 (CAS500-4), also known as Agriculture and Forestry Satellite, in terms of spectral wavelengths and spatial resolution. This letter examined cloud detection for later use in the CAS500-4 based on deep learning technologies. DeepLabV3+, a traditional Convolutional Neural Network (CNN) model, and Shifted Windows (Swin) Transformer, a state-of-the-art (SOTA) Transformer model, were compared using 22,728 images provided by Radiant Earth Foundation (REF). Swin Transformer showed a better performance with a precision of 0.886 and a recall of 0.875, which is a balanced result, unbiased between over- and under-estimation. Deep learning-based cloud detection is expected to be a future operational module for CAS500-4 through optimization for the Korean Peninsula.