• Title/Summary/Keyword: 학습 데이터

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Review of Land Cover Classification Potential in River Spaces Using Satellite Imagery and Deep Learning-Based Image Training Method (딥 러닝 기반 이미지 트레이닝을 활용한 하천 공간 내 피복 분류 가능성 검토)

  • Woochul, Kang;Eun-kyung, Jang
    • Ecology and Resilient Infrastructure
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    • v.9 no.4
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    • pp.218-227
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    • 2022
  • This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

Optimization of Approximate Modular Multiplier for R-LWE Cryptosystem (R-LWE 암호화를 위한 근사 모듈식 다항식 곱셈기 최적화)

  • Jae-Woo, Lee;Youngmin, Kim
    • Journal of IKEEE
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    • v.26 no.4
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    • pp.736-741
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    • 2022
  • Lattice-based cryptography is the most practical post-quantum cryptography because it enjoys strong worst-case security, relatively efficient implementation, and simplicity. Ring learning with errors (R-LWE) is a public key encryption (PKE) method of lattice-based encryption (LBC), and the most important operation of R-LWE is the modular polynomial multiplication of rings. This paper proposes a method for optimizing modular multipliers based on approximate computing (AC) technology, targeting the medium-security parameter set of the R-LWE cryptosystem. First, as a simple way to implement complex logic, LUT is used to omit some of the approximate multiplication operations, and the 2's complement method is used to calculate the number of bits whose value is 1 when converting the value of the input data to binary. We propose a total of two methods to reduce the number of required adders by minimizing them. The proposed LUT-based modular multiplier reduced both speed and area by 9% compared to the existing R-LWE modular multiplier, and the modular multiplier using the 2's complement method reduced the area by 40% and improved the speed by 2%. appear. Finally, the area of the optimized modular multiplier with both of these methods applied was reduced by up to 43% compared to the previous one, and the speed was reduced by up to 10%.

Grading of Harvested 'Mihwang' Peach Maturity with Convolutional Neural Network (합성곱 신경망을 이용한 '미황' 복숭아 과실의 성숙도 분류)

  • Shin, Mi Hee;Jang, Kyeong Eun;Lee, Seul Ki;Cho, Jung Gun;Song, Sang Jun;Kim, Jin Gook
    • Journal of Bio-Environment Control
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    • v.31 no.4
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    • pp.270-278
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    • 2022
  • This study was conducted using deep learning technology to classify for 'Mihwang' peach maturity with RGB images and fruit quality attributes during fruit development and maturation periods. The 730 images of peach were used in the training data set and validation data set at a ratio of 8:2. The remains of 170 images were used to test the deep learning models. In this study, among the fruit quality attributes, firmness, Hue value, and a* value were adapted to the index with maturity classification, such as immature, mature, and over mature fruit. This study used the CNN (Convolutional Neural Networks) models for image classification; VGG16 and InceptionV3 of GoogLeNet. The performance results show 87.1% and 83.6% with Hue left value in VGG16 and InceptionV3, respectively. In contrast, the performance results show 72.2% and 76.9% with firmness in VGG16 and InceptionV3, respectively. The loss rate shows 54.3% and 62.1% with firmness in VGG16 and InceptionV3, respectively. It considers increasing for adapting a field utilization with firmness index in peach.

Low Power ADC Design for Mixed Signal Convolutional Neural Network Accelerator (혼성신호 컨볼루션 뉴럴 네트워크 가속기를 위한 저전력 ADC설계)

  • Lee, Jung Yeon;Asghar, Malik Summair;Arslan, Saad;Kim, HyungWon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.11
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    • pp.1627-1634
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    • 2021
  • This paper introduces a low-power compact ADC circuit for analog Convolutional filter for low-power neural network accelerator SOC. While convolutional neural network accelerators can speed up the learning and inference process, they have drawback of consuming excessive power and occupying large chip area due to large number of multiply-and-accumulate operators when implemented in complex digital circuits. To overcome these drawbacks, we implemented an analog convolutional filter that consists of an analog multiply-and-accumulate arithmetic circuit along with an ADC. This paper is focused on the design optimization of a low-power 8bit SAR ADC for the analog convolutional filter accelerator We demonstrate how to minimize the capacitor-array DAC, an important component of SAR ADC, which is three times smaller than the conventional circuit. The proposed ADC has been fabricated in CMOS 65nm process. It achieves an overall size of 1355.7㎛2, power consumption of 2.6㎼ at a frequency of 100MHz, SNDR of 44.19 dB, and ENOB of 7.04bit.

Peak Impact Force of Ship Bridge Collision Based on Neural Network Model (신경망 모델을 이용한 선박-교각 최대 충돌력 추정 연구)

  • Wang, Jian;Noh, Jackyou
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.28 no.1
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    • pp.175-183
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    • 2022
  • The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.

Development of Mask-RCNN Based Axle Control Violation Detection Method for Enforcement on Overload Trucks (과적 화물차 단속을 위한 Mask-RCNN기반 축조작 검지 기술 개발)

  • Park, Hyun suk;Cho, Yong sung;Kim, Young Nam;Kim, Jin pyung
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.21 no.5
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    • pp.57-66
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    • 2022
  • The Road Management Administration is cracking down on overloaded vehicles by installing low-speed or high-speed WIMs at toll gates and main lines on expressways. However, in recent years, the act of intelligently evading the overloaded-vehicle control system of the Road Management Administration by illegally manipulating the variable axle of an overloaded truck is increasing. In this manipulation, when entering the overloaded-vehicle checkpoint, all axles of the vehicle are lowered to pass normally, and when driving on the main road, the variable axle of the vehicle is illegally lifted with the axle load exceeding 10 tons alarmingly. Therefore, this study developed a technology to detect the state of the variable axle of a truck driving on the road using roadside camera images. In particular, this technology formed the basis for cracking down on overloaded vehicles by lifting the variable axle after entering the checkpoint and linking the vehicle with the account information of the checkpoint. Fundamentally, in this study, the tires of the vehicle were recognized using the Mask RCNN algorithm, the recognized tires were virtually arranged before and after the checkpoint, and the height difference of the vehicle was measured from the arrangement to determine whether the variable axle was lifted after the vehicle left the checkpoint.

Prediction of Stacking Angles of Fiber-reinforced Composite Materials Using Deep Learning Based on Convolutional Neural Networks (합성곱 신경망 기반의 딥러닝을 이용한 섬유 강화 복합재료의 적층 각도 예측)

  • Hyunsoo Hong;Wonki Kim;Do Yoon Jeon;Kwanho Lee;Seong Su Kim
    • Composites Research
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    • v.36 no.1
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    • pp.48-52
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    • 2023
  • Fiber-reinforced composites have anisotropic material properties, so the mechanical properties of composite structures can vary depending on the stacking sequence. Therefore, it is essential to design the proper stacking sequence of composite structures according to the functional requirements. However, depending on the manufacturing condition or the shape of the structure, there are many cases where the designed stacking angle is out of range, which can affect structural performance. Accordingly, it is important to analyze the stacking angle in order to confirm that the composite structure is correctly fabricated as designed. In this study, the stacking angle was predicted from real cross-sectional images of fiber-reinforced composites using convolutional neural network (CNN)-based deep learning. Carbon fiber-reinforced composite specimens with several stacking angles were fabricated and their cross-sections were photographed on a micro-scale using an optical microscope. The training was performed for a CNN-based deep learning model using the cross-sectional image data of the composite specimens. As a result, the stacking angle can be predicted from the actual cross-sectional image of the fiber-reinforced composite with high accuracy.

Open Domain Machine Reading Comprehension using InferSent (InferSent를 활용한 오픈 도메인 기계독해)

  • Jeong-Hoon, Kim;Jun-Yeong, Kim;Jun, Park;Sung-Wook, Park;Se-Hoon, Jung;Chun-Bo, Sim
    • Smart Media Journal
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    • v.11 no.10
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    • pp.89-96
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    • 2022
  • An open domain machine reading comprehension is a model that adds a function to search paragraphs as there are no paragraphs related to a given question. Document searches have an issue of lower performance with a lot of documents despite abundant research with word frequency based TF-IDF. Paragraph selections also have an issue of not extracting paragraph contexts, including sentence characteristics accurately despite a lot of research with word-based embedding. Document reading comprehension has an issue of slow learning due to the growing number of parameters despite a lot of research on BERT. Trying to solve these three issues, this study used BM25 which considered even sentence length and InferSent to get sentence contexts, and proposed an open domain machine reading comprehension with ALBERT to reduce the number of parameters. An experiment was conducted with SQuAD1.1 datasets. BM25 recorded a higher performance of document research than TF-IDF by 3.2%. InferSent showed a higher performance in paragraph selection than Transformer by 0.9%. Finally, as the number of paragraphs increased in document comprehension, ALBERT was 0.4% higher in EM and 0.2% higher in F1.

Development and Evaluation of Safe Route Service of Electric Personal Assistive Mobility Devices for the Mobility Impaired People (교통약자를 위한 전동 이동 보조기기 안전 경로 서비스의 개발과 평가)

  • Je-Seung WOO;Sun-Gi HONG;Sang-Kyoung YOO;Hoe Kyoung KIM
    • Journal of the Korean Association of Geographic Information Studies
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    • v.26 no.3
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    • pp.85-96
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    • 2023
  • This study developed and evaluated a safe route guidance service for electric personal assistive mobility device used mainly by the mobility impaired people to improve their mobility. Thirteen underlying factors affecting the mobility of electric personal assistive mobility device have been derived through a survey with the mobility impaired people and employees in related organizations in Busan Metropolitan City. After assigning safety scores to individual factors and identifying the relevant factors along routes of interest with an object detection AI model, the safe route for electric personal assistive mobility device was provided through an optimal path-finding algorithm. As a result of comparing the general route of T-map and the recommended route of this study for the identical routes, the latter had relatively fewer obstacles and the gentler slope than the former, implicating that the recommended route is safer than the general one. As future works, it is necessary to enhance the function of a route guidance service based on the real-time location of users and to conduct spot investigations to evaluate and verify its social acceptability.

An Approach Using LSTM Model to Forecasting Customer Congestion Based on Indoor Human Tracking (실내 사람 위치 추적 기반 LSTM 모델을 이용한 고객 혼잡 예측 연구)

  • Hee-ju Chae;Kyeong-heon Kwak;Da-yeon Lee;Eunkyung Kim
    • Journal of the Korea Society for Simulation
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    • v.32 no.3
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    • pp.43-53
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    • 2023
  • In this detailed and comprehensive study, our primary focus has been placed on accurately gauging the number of visitors and their real-time locations in commercial spaces. Particularly, in a real cafe, using security cameras, we have developed a system that can offer live updates on available seating and predict future congestion levels. By employing YOLO, a real-time object detection and tracking algorithm, the number of visitors and their respective locations in real-time are also monitored. This information is then used to update a cafe's indoor map, thereby enabling users to easily identify available seating. Moreover, we developed a model that predicts the congestion of a cafe in real time. The sophisticated model, designed to learn visitor count and movement patterns over diverse time intervals, is based on Long Short Term Memory (LSTM) to address the vanishing gradient problem and Sequence-to-Sequence (Seq2Seq) for processing data with temporal relationships. This innovative system has the potential to significantly improve cafe management efficiency and customer satisfaction by delivering reliable predictions of cafe congestion to all users. Our groundbreaking research not only demonstrates the effectiveness and utility of indoor location tracking technology implemented through security cameras but also proposes potential applications in other commercial spaces.