• Title/Summary/Keyword: GADF

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FINE SEGMENTATION USING GEOMETRIC ATTRACTION-DRIVEN FLOW AND EDGE-REGIONS

  • Hahn, Joo-Young;Lee, Chang-Ock
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.11 no.2
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    • pp.41-47
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    • 2007
  • A fine segmentation algorithm is proposed for extracting objects in an image, which have both weak boundaries and highly non-convex shapes. The image has simple background colors or simple object colors. Two concepts, geometric attraction-driven flow (GADF) and edge-regions are combined to detect boundaries of objects in a sub-pixel resolution. The main strategy to segment the boundaries is to construct initial curves close to objects by using edge-regions and then to make a curve evolution in GADF. Since the initial curves are close to objects regardless of shapes, highly non-convex shapes are easily detected and dependence on initial curves in boundary-based segmentation algorithms is naturally removed. Weak boundaries are also detected because the orientation of GADF is obtained regardless of the strength of boundaries. For a fine segmentation, we additionally propose a local region competition algorithm to detect perceptible boundaries which are used for the extraction of objects without visual loss of detailed shapes. We have successfully accomplished the fine segmentation of objects from images taken in the studio and aphids from images of soybean leaves.

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Classification Method of Multi-State Appliances in Non-intrusive Load Monitoring Environment based on Gramian Angular Field (Gramian angular field 기반 비간섭 부하 모니터링 환경에서의 다중 상태 가전기기 분류 기법)

  • Seon, Joon-Ho;Sun, Young-Ghyu;Kim, Soo-Hyun;Kyeong, Chanuk;Sim, Issac;Lee, Heung-Jae;Kim, Jin-Young
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.3
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    • pp.183-191
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    • 2021
  • Non-intrusive load monitoring is a technology that can be used for predicting and classifying the type of appliances through real-time monitoring of user power consumption, and it has recently got interested as a means of energy-saving. In this paper, we propose a system for classifying appliances from user consumption data by combining GAF(Gramian angular field) technique that can be used for converting one-dimensional data to the two-dimensional matrix with convolutional neural networks. We use REDD(residential energy disaggregation dataset) that is the public appliances power data and confirm the classification accuracy of the GASF(Gramian angular summation field) and GADF(Gramian angular difference field). Simulation results show that both models showed 94% accuracy on appliances with binary-state(on/off) and that GASF showed 93.5% accuracy that is 3% higher than GADF on appliances with multi-state. In later studies, we plan to increase the dataset and optimize the model to improve accuracy and speed.

MPIL: Market prediction through image learning of unstructured and structured data (비정형, 정형 데이터의 이미지 학습을 활용한 시장예측)

  • Lee, Yoon Seon;Lee, Ju Hong;Choi, Bum Ghi;Song, Jae Won
    • Smart Media Journal
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    • v.10 no.2
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    • pp.16-21
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    • 2021
  • Financial time series analysis plays a very important role economically and socially in modern society and is an important task affecting global development, but due to difficulties such as a lot of noise and uncertainty, financial time series analysis prediction is a difficult research topic. In this paper, we propose a market prediction method (MPIL) by converting unstructured data and structured data into images. For market prediction, it analyzes SNS and news data, which is unstructured data for n days, and converts the market data, which is structured data, to an image with the GADF algorithm, and predicts an ultra-short market that predicts the price of n+1 days through image learning. MPIL has an average accuracy of 56%, which is higher than the 50% average accuracy of the model that predicts the market with LSTM by using sentiment analysis used for existing market forecasting.