• Title/Summary/Keyword: Deep Learning based System

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Unsupervised learning-based automated patent document classification system (비지도학습 기반 자동 특허문서 분류 시스템)

  • Kim, Sang-Baek;Kim, Ji-Ho;Lee, Hong-Chul
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2021.07a
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    • pp.421-422
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    • 2021
  • 국내·외 기업들의 기술을 보호하고자 매년 100만개의 특허가 출원되고 있다. 등록된 특허 수가 증가될수록 전문가의 판단만으로 원하는 기술 분야의 유효한 특허문서를 선별하는 것은 효율적이지 않으며 객관적인 결과를 기대하기 어려워진다. 본 연구에서는 유효 특허문서 분류 정확성과 전문가의 업무 효율성을 제고하고자 비지도학습 모델인 잠재 디리클레 할당 알고리즘(Latent Dirichlet Allocation, LDA)과 딥러닝을 활용하여 자동 특허문서 분류 시스템을 제안하고자 한다.

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Classification and analysis of error types for deep learning-based Korean spelling correction (딥러닝 기반 한국어 맞춤법 교정을 위한 오류 유형 분류 및 분석)

  • Koo, Seonmin;Park, Chanjun;So, Aram;Lim, Heuiseok
    • Journal of the Korea Convergence Society
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    • v.12 no.12
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    • pp.65-74
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    • 2021
  • Recently, studies on Korean spelling correction have been actively conducted based on machine translation and automatic noise generation. These methods generate noise and use as train and data set. This has limitation in that it is difficult to accurately measure performance because it is unlikely that noise other than the noise used for learning is included in the test set In addition, there is no practical error type standard, so the type of error used in each study is different, making qualitative analysis difficult. This paper proposes new 'error type classification' for deep learning-based Korean spelling correction research, and error analysis perform on existing commercialized Korean spelling correctors (System A, B, C). As a result of analysis, it was found the three correction systems did not perform well in correcting other error types presented in this paper other than spacing, and hardly recognized errors in word order or tense.

Abnormal behaviour in rock bream (Oplegnathus fasciatus) detected using deep learning-based image analysis

  • Jang, Jun-Chul;Kim, Yeo-Reum;Bak, SuHo;Jang, Seon-Woong;Kim, Jong-Myoung
    • Fisheries and Aquatic Sciences
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    • v.25 no.3
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    • pp.151-157
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    • 2022
  • Various approaches have been applied to transform aquaculture from a manual, labour-intensive industry to one dependent on automation technologies in the era of the fourth industrial revolution. Technologies associated with the monitoring of physical condition have successfully been applied in most aquafarm facilities; however, real-time biological monitoring systems that can observe fish condition and behaviour are still required. In this study, we used a video recorder placed on top of a fish tank to observe the swimming patterns of rock bream (Oplegnathus fasciatus), first one fish alone and then a group of five fish. Rock bream in the video samples were successfully identified using the you-only-look-once v3 algorithm, which is based on the Darknet-53 convolutional neural network. In addition to recordings of swimming behaviour under normal conditions, the swimming patterns of fish under abnormal conditions were recorded on adding an anaesthetic or lowering the salinity. The abnormal conditions led to changes in the velocity of movement (3.8 ± 0.6 cm/s) involving an initial rapid increase in speed (up to 16.5 ± 3.0 cm/s, upon 2-phenoxyethanol treatment) before the fish stopped moving, as well as changing from swimming upright to dying lying on their sides. Machine learning was applied to datasets consisting of normal or abnormal behaviour patterns, to evaluate the fish behaviour. The proposed algorithm showed a high accuracy (98.1%) in discriminating normal and abnormal rock bream behaviour. We conclude that artificial intelligence-based detection of abnormal behaviour can be applied to develop an automatic bio-management system for use in the aquaculture industry.

Learning Source Code Context with Feature-Wise Linear Modulation to Support Online Judge System (온라인 저지 시스템 지원을 위한 Feature-Wise Linear Modulation 기반 소스코드 문맥 학습 모델 설계)

  • Hyun, Kyeong-Seok;Choi, Woosung;Chung, Jaehwa
    • KIPS Transactions on Software and Data Engineering
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    • v.11 no.11
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    • pp.473-478
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    • 2022
  • Evaluation learning based on code testing is becoming a popular solution in programming education via Online judge(OJ). In the recent past, many papers have been published on how to detect plagiarism through source code similarity analysis to support OJ. However, deep learning-based research to support automated tutoring is insufficient. In this paper, we propose Input & Output side FiLM models to predict whether the input code will pass or fail. By applying Feature-wise Linear Modulation(FiLM) technique to GRU, our model can learn combined information of Java byte codes and problem information that it tries to solve. On experimental design, a balanced sampling technique was applied to evenly distribute the data due to the occurrence of asymmetry in data collected by OJ. Among the proposed models, the Input Side FiLM model showed the highest performance of 73.63%. Based on result, it has been shown that students can check whether their codes will pass or fail before receiving the OJ evaluation which could provide basic feedback for improvements.

Deep Learning-based Indoor Positioning System Using CSI (채널 상태 정보를 이용한 딥 러닝 기반 실내 위치 확인 시스템)

  • Zhang, Zhongfeng;Choi, Seungwon
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.16 no.4
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    • pp.1-7
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    • 2020
  • Over the past few years, Wi-Fi signal based indoor positioning system (IPS) has been researched extensively because of its low expenses of infrastructure deployment. There are two major aspects of location-related information contained in Wi-Fi signals. One is channel state information (CSI), and one is received signal strength indicator (RSSI). Compared to the RSSI, the CSI has been widely utilized because it is able to reveal fine-grained information related to locations. However, the conventional IPS that employs a single access point (AP) does not exhibit decent performance especially in the environment of non-line-of-sight (NLOS) situations due to the reliability degeneration of signals caused by multipath fading effect. In order to address this problem, in this paper, we propose a novel method that utilizes multiple APs instead of a single AP to enhance the robustness of the IPS. In our proposed method, a hybrid neural network is applied to the CSIs collected from multiple APs. By relying more on the fingerprint constructed by the CSI collected from an AP that is less affected by the NLOS, we find that the performance of the IPS is significantly improved.

Development of Real-time Video Search System Using the Intelligent Object Recognition Technology (지능형 객체 인식 기술을 이용한 실시간 동영상 검색시스템)

  • Chang, Jae-Young;Kang, Chan-Hyeok;Yoon, Jae-Min;Cho, Jae-Won;Jung, Ji-Sung;Chun, Jonghoon
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.20 no.6
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    • pp.85-91
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    • 2020
  • Recently, video-taping equipment such as CCTV have been seeing more use for crime prevention and general safety concerns. Since these video-taping equipment operates all throughout the day, the need for security personnel is lessened, and naturally costs incurred from managing such manpower should also decrease. However, technology currently used predominantly lacks self-sufficiency when given the task of searching for a specific object in the recorded video such as a person, and has to be done manually; current security-based video equipment is insufficient in an environment where real-time information retrieval is required. In this paper, we propose a technology that uses the latest deep-learning technology and OpenCV library to quickly search for a specific person in a video; the search is based on the clothing information that is inputted by the user and transmits the result in real time. We implemented our system to automatically recognize specific human objects in real time by using the YOLO library, whilst deep learning technology is used to classify human clothes into top/bottom clothes. Colors are also detected through the OpenCV library which are then all combined to identify the requested object. The system presented in this paper not only accurately and quickly recognizes a person object with a specific clothing, but also has a potential extensibility that can be used for other types of object recognition in a video surveillance system for various purposes.

A Study on Deep Learning based Aerial Vehicle Classification for Armament Selection (무장 선택을 위한 딥러닝 기반의 비행체 식별 기법 연구)

  • Eunyoung, Cha;Jeongchang, Kim
    • Journal of Broadcast Engineering
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    • v.27 no.6
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    • pp.936-939
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    • 2022
  • As air combat system technologies developed in recent years, the development of air defense systems is required. In the operating concept of the anti-aircraft defense system, selecting an appropriate armament for the target is one of the system's capabilities in efficiently responding to threats using limited anti-aircraft power. Much of the flying threat identification relies on the operator's visual identification. However, there are many limitations in visually discriminating a flying object maneuvering high speed from a distance. In addition, as the demand for unmanned and intelligent weapon systems on the modern battlefield increases, it is essential to develop a technology that automatically identifies and classifies the aircraft instead of the operator's visual identification. Although some examples of weapon system identification with deep learning-based models by collecting video data for tanks and warships have been presented, aerial vehicle identification is still lacking. Therefore, in this paper, we present a model for classifying fighters, helicopters, and drones using a convolutional neural network model and analyze the performance of the presented model.

Deep learning-based Multilingual Sentimental Analysis using English Review Data (영어 리뷰데이터를 이용한 딥러닝 기반 다국어 감성분석)

  • Sung, Jae-Kyung;Kim, Yung Bok;Kim, Yong-Guk
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.19 no.3
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    • pp.9-15
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    • 2019
  • Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.

Development of deep learning algorithm for classification of disc cutter wear condition based on real-time measurement data (실시간 측정데이터 기반의 디스크커터 마모상태 판별 딥러닝 알고리즘 개발)

  • Ji Yun Lee;Byung Chul Yeo;Ho Young Jeong;Jung Joo Kim
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.26 no.3
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    • pp.281-301
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    • 2024
  • The power cable tunnels which are part of the underground transmission line project, are constructed using the shield TBM method. The disc cutter among the shield TBM components plays an important role in breaking rock mass. Efficient tunnel construction is possible only when appropriate replacement occurs as the wear limit is reached or damage such as uneven wear occurs. A study was conducted to determine the wear conditions of disc cutter using a deep learning algorithm based on real-time measurement data of wear and rotation speed. Based on the results of full-scaled tunnelling tests, it was confirmed that measurement data was obtained differently depending on the wear conditions of disc cutter. Using real-time measurement data, an algorithm was developed to determine disc cutter wear characteristics based on a convolutional neural network model. Distributional patterns of data can be learned through CNN filters, and the performance of the model that can classify uniform wear and uneven wear through these pattern features.

Automatic Construction of Deep Learning Training Data for High-Definition Road Maps Using Mobile Mapping System (정밀도로지도 제작을 위한 모바일매핑시스템 기반 딥러닝 학습데이터의 자동 구축)

  • Choi, In Ha;Kim, Eui Myoung
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.39 no.3
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    • pp.133-139
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    • 2021
  • Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.