• Title/Summary/Keyword: 인공지능-딥러닝

Search Result 699, Processing Time 0.025 seconds

Implementation of Pre-Post Process for Accuraty Improvement of OCR Recognition Engine Based on Deep-Learning Technology (딥러닝 기반 OCR 인식 엔진의 정확도 향상을 위한 전/후처리기 기술 구현)

  • Jang, Chang-Bok;Kim, Ki-Bong
    • Journal of Convergence for Information Technology
    • /
    • v.12 no.1
    • /
    • pp.163-170
    • /
    • 2022
  • With the advent of the 4th Industrial Revolution, solutions that apply AI technology are being actively developed. Since 2017, the introduction of business automation solutions using AI-based Robotic Process Automation (RPA) has begun in the financial sector and insurance companies, and recently, it is entering a time when it spreads past the stage of introducing RPA solutions. Among the business automation using these RPA solutions, it is very important how accurately textual information in the document is recognized for business automation using various documents. Such character recognition has recently increased its accuracy by introducing deep learning technology, but there is still no recognition model with perfect recognition accuracy. Therefore, in this paper, we checked how much accuracy is improved when pre- and post-processor technologies are applied to deep learning-based character recognition engines, and implemented RPA recognition engines and linkage technologies.

Development of a Deep Learning Algorithm for Anomaly Detection of Manufacturing Facility (설비 이상탐지를 위한 딥러닝 알고리즘 개발)

  • Kim, Min-Hee;Jin, Kyo-Hong
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.26 no.2
    • /
    • pp.199-206
    • /
    • 2022
  • A malfunction or breakdown of a manufacturing facility leads to product defects and the suspension of production lines, resulting in huge financial losses for manufacturers. Due to the spread of smart factory services, a large amount of data is being collected in factories, and AI-based research is being conducted to predict and diagnose manufacturing facility breakdowns or manufacturing site efficiency. However, because of the characteristics of manufacturing data, such as a severe class imbalance about abnormalities and ambiguous label information that distinguishes abnormalities, developing classification or anomaly detection models is highly difficult. In this paper, we present an deep learning algorithm for anomaly detection of a manufacturing facility using reconstruction loss of CNN-based model and ananlyze its performance. The algorithm detects anomalies by relying solely on normal data from the facility's manufacturing data in the exclusion of abnormal data.

Deep learning-based clothing attribute classification using fashion image data (패션 이미지 데이터를 활용한 딥러닝 기반의 의류속성 분류)

  • Hye Seon Jeong;So Young Lee;Choong Kwon Lee
    • Smart Media Journal
    • /
    • v.13 no.4
    • /
    • pp.57-64
    • /
    • 2024
  • Attributes such as material, color, and fit in fashion images are important factors for consumers to purchase clothing. However, the process of classifying clothing attributes requires a large amount of manpower and is inconsistent because it relies on the subjective judgment of human operators. To alleviate this problem, there is a need for research that utilizes artificial intelligence to classify clothing attributes in fashion images. Previous studies have mainly focused on classifying clothing attributes for either tops or bottoms, so there is a limitation that the attributes of both tops and bottoms cannot be identified simultaneously in the case of full-body fashion images. In this study, we propose a deep learning model that can distinguish between tops and bottoms in fashion images and classify the category of each item and the attributes of the clothing material. The deep learning models ResNet and EfficientNet were used in this study, and the dataset used for training was 1,002,718 fashion images and 125 labels including clothing categories and material properties. Based on the weighted F1-Score, ResNet is 0.800 and EfficientNet is 0.781, with ResNet showing better performance.

A Study on Performance Improvement of Recurrent Neural Networks Algorithm using Word Group Expansion Technique (단어그룹 확장 기법을 활용한 순환신경망 알고리즘 성능개선 연구)

  • Park, Dae Seung;Sung, Yeol Woo;Kim, Cheong Ghil
    • Journal of Industrial Convergence
    • /
    • v.20 no.4
    • /
    • pp.23-30
    • /
    • 2022
  • Recently, with the development of artificial intelligence (AI) and deep learning, the importance of conversational artificial intelligence chatbots is being highlighted. In addition, chatbot research is being conducted in various fields. To build a chatbot, it is developed using an open source platform or a commercial platform for ease of development. These chatbot platforms mainly use RNN and application algorithms. The RNN algorithm has the advantages of fast learning speed, ease of monitoring and verification, and good inference performance. In this paper, a method for improving the inference performance of RNNs and applied algorithms was studied. The proposed method used the word group expansion learning technique of key words for each sentence when RNN and applied algorithm were applied. As a result of this study, the RNN, GRU, and LSTM three algorithms with a cyclic structure achieved a minimum of 0.37% and a maximum of 1.25% inference performance improvement. The research results obtained through this study can accelerate the adoption of artificial intelligence chatbots in related industries. In addition, it can contribute to utilizing various RNN application algorithms. In future research, it will be necessary to study the effect of various activation functions on the performance improvement of artificial neural network algorithms.

Analysis of the Status of Natural Language Processing Technology Based on Deep Learning (딥러닝 중심의 자연어 처리 기술 현황 분석)

  • Park, Sang-Un
    • The Journal of Bigdata
    • /
    • v.6 no.1
    • /
    • pp.63-81
    • /
    • 2021
  • The performance of natural language processing is rapidly improving due to the recent development and application of machine learning and deep learning technologies, and as a result, the field of application is expanding. In particular, as the demand for analysis on unstructured text data increases, interest in NLP(Natural Language Processing) is also increasing. However, due to the complexity and difficulty of the natural language preprocessing process and machine learning and deep learning theories, there are still high barriers to the use of natural language processing. In this paper, for an overall understanding of NLP, by examining the main fields of NLP that are currently being actively researched and the current state of major technologies centered on machine learning and deep learning, We want to provide a foundation to understand and utilize NLP more easily. Therefore, we investigated the change of NLP in AI(artificial intelligence) through the changes of the taxonomy of AI technology. The main areas of NLP which consists of language model, text classification, text generation, document summarization, question answering and machine translation were explained with state of the art deep learning models. In addition, major deep learning models utilized in NLP were explained, and data sets and evaluation measures for performance evaluation were summarized. We hope researchers who want to utilize NLP for various purposes in their field be able to understand the overall technical status and the main technologies of NLP through this paper.

A Study on Combine Artificial Intelligence Models for multi-classification for an Abnormal Behaviors in CCTV images (CCTV 영상의 이상행동 다중 분류를 위한 결합 인공지능 모델에 관한 연구)

  • Lee, Hongrae;Kim, Youngtae;Seo, Byung-suk
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2022.05a
    • /
    • pp.498-500
    • /
    • 2022
  • CCTV protects people and assets safely by identifying dangerous situations and responding promptly. However, it is difficult to continuously monitor the increasing number of CCTV images. For this reason, there is a need for a device that continuously monitors CCTV images and notifies when abnormal behavior occurs. Recently, many studies using artificial intelligence models for image data analysis have been conducted. This study simultaneously learns spatial and temporal characteristic information between image data to classify various abnormal behaviors that can be observed in CCTV images. As an artificial intelligence model used for learning, we propose a multi-classification deep learning model that combines an end-to-end 3D convolutional neural network(CNN) and ResNet.

  • PDF

A Conceptual Architecture and its Experimental Validation of CCTV-Video Object Activitization for Tangible Assets of Experts' Visual Knowledge in Smart Factories (고숙련자 공장작업지식 자산화를 위한 CCTV-동영상 객체능동화의 개념적 아키텍처와 실험적 검증)

  • Eun-Bi Cho;Dinh-Lam Pham;Kyung-Hee Sun;Kwanghoon Pio Kim
    • Journal of Internet Computing and Services
    • /
    • v.25 no.2
    • /
    • pp.101-111
    • /
    • 2024
  • In this paper, we propose a concpetual architecture and its implementation approach for contextualizing unstructured CCTV-video frame data into structured XML-video textual data by using the deep-learning neural network models and frameworks. Conclusively, through the conceptual architecture and the implementation approach proposed in this paper, we can eventually realize and implement the so-called sharable working and experiencing knowledge management platforms to be adopted to smart factories in various industries.

Efficient Object Recognition by Masking Semantic Pixel Difference Region of Vision Snapshot for Lightweight Embedded Systems (경량화된 임베디드 시스템에서 의미론적인 픽셀 분할 마스킹을 이용한 효율적인 영상 객체 인식 기법)

  • Yun, Heuijee;Park, Daejin
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.26 no.6
    • /
    • pp.813-826
    • /
    • 2022
  • AI-based image processing technologies in various fields have been widely studied. However, the lighter the board, the more difficult it is to reduce the weight of image processing algorithm due to a lot of computation. In this paper, we propose a method using deep learning for object recognition algorithm in lightweight embedded boards. We can determine the area using a deep neural network architecture algorithm that processes semantic segmentation with a relatively small amount of computation. After masking the area, by using more accurate deep learning algorithm we could operate object detection with improved accuracy for efficient neural network (ENet) and You Only Look Once (YOLO) toward executing object recognition in real time for lightweighted embedded boards. This research is expected to be used for autonomous driving applications, which have to be much lighter and cheaper than the existing approaches used for object recognition.

Research Trend of the Remote Sensing Image Analysis Using Deep Learning (딥러닝을 이용한 원격탐사 영상분석 연구동향)

  • Kim, Hyungwoo;Kim, Minho;Lee, Yangwon
    • Korean Journal of Remote Sensing
    • /
    • v.38 no.5_3
    • /
    • pp.819-834
    • /
    • 2022
  • Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

Unification of Deep Learning Model trained by Parallel Learning in Security environment

  • Lee, Jong-Lark
    • Journal of the Korea Society of Computer and Information
    • /
    • v.26 no.12
    • /
    • pp.69-75
    • /
    • 2021
  • Recently, deep learning, which is the most used in the field of artificial intelligence, has a structure that is gradually becoming larger and more complex. As the deep learning model grows, a large amount of data is required to learn it, but there are cases in which it is difficult to integrate and learn the data because the data is distributed among several owners and security issues. In that situation we conducted parallel learning for each users that own data and then studied how to integrate it. For this, distributed learning was performed for each owner assuming the security situation as V-environment and H-environment, and the results of distributed learning were integrated using Average, Max, and AbsMax. As a result of applying this to the mnist-fashion data, it was confirmed that there was no significant difference from the results obtained by integrating the data in the V-environment in terms of accuracy. In the H-environment, although there was a difference, meaningful results were obtained.