• Title/Summary/Keyword: integrated extraction

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POSE-VIWEPOINT ADAPTIVE OBJECT TRACKING VIA ONLINE LEARNING APPROACH

  • Mariappan, Vinayagam;Kim, Hyung-O;Lee, Minwoo;Cho, Juphil;Cha, Jaesang
    • International journal of advanced smart convergence
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    • v.4 no.2
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    • pp.20-28
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    • 2015
  • In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

The Design and Implementation of School-Zone Safety Management System Based onContext-Aware (상황인식 기반의 스쿨존 안전 관리 시스템 설계 및 구현)

  • Lee, Jin-Kwan;Lee, Chang-Bok;Park, Sang-Jun;Lee, Jong-Chan;Park, Ki-Hong
    • Convergence Security Journal
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    • v.9 no.1
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    • pp.11-17
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    • 2009
  • The object of this paper is to design a school-zone safety management system based on context-aware, integrated with computing technology. When it occurs to kidnap of elementary school students, the monitoring device creates context information through a combination object extraction and context-aware technology and alarm administrator about an emergency situation. In addition, the proposed system that requires a human perspective, a railroad crossing, statistics research of traffic, and a variety of applications such as factory automation systems can be used to be the best choice.

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Integration of Multi-spectral Remote Sensing Images and GIS Thematic Data for Supervised Land Cover Classification

  • Jang Dong-Ho;Chung Chang-Jo F
    • Korean Journal of Remote Sensing
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    • v.20 no.5
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    • pp.315-327
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    • 2004
  • Nowadays, interests in land cover classification using not only multi-sensor images but also thematic GIS information are increasing. Often, although useful GIS information for the classification is available, the traditional MLE (maximum likelihood estimation techniques) does not allow us to use the information, due to the fact that it cannot handle the GIS data properly. This paper propose two extended MLE algorithms that can integrate both remote sensing images and GIS thematic data for land-cover classification. They include modified MLE and Bayesian predictive likelihood estimation technique (BPLE) techniques that can handle both categorical GIS thematic data and remote sensing images in an integrated manner. The proposed algorithms were evaluated through supervised land-cover classification with Landsat ETM+ images and an existing land-use map in the Gongju area, Korea. As a result, the proposed method showed considerable improvements in classification accuracy, when compared with other multi-spectral classification techniques. The integration of remote sensing images and the land-use map showed that overall accuracy indicated an improvement in classification accuracy of 10.8% when using MLE, and 9.6% for the BPLE. The case study also showed that the proposed algorithms enable the extraction of the area with land-cover change. In conclusion, land cover classification results produced through the integration of various GIS spatial data and multi-spectral images, will be useful to involve complementary data to make more accurate decisions.

Speeding up the KLT Tracker for Real-time Image Georeferencing using GPS/INS Data

  • Tanathong, Supannee;Lee, Im-Pyeong
    • Korean Journal of Remote Sensing
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    • v.26 no.6
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    • pp.629-644
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    • 2010
  • A real-time image georeferencing system requires all inputs to be determined in real-time. The intrinsic camera parameters can be identified in advance from a camera calibration process while other control information can be derived instantaneously from real-time GPS/INS data. The bottleneck process is tie point acquisition since manual operations will be definitely obstacles for real-time system while the existing extraction methods are not fast enough. In this paper, we present a fast-and-automated image matching technique based on the KLT tracker to obtain a set of tie-points in real-time. The proposed work accelerates the KLT tracker by supplying the initial guessed tie-points computed using the GPS/INS data. Originally, the KLT only works effectively when the displacement between tie-points is small. To drive an automated solution, this paper suggests an appropriate number of depth levels for multi-resolution tracking under large displacement using the knowledge of uncertainties the GPS/INS data measurements. The experimental results show that our suggested depth levels is promising and the proposed work can obtain tie-points faster than the ordinary KLT by 13% with no less accuracy. This promising result suggests that our proposed algorithm can be effectively integrated into the real-time image georeferencing for further developing a real-time surveillance application.

Developing a Method to Define Mountain Search Priority Areas Based on Behavioral Characteristics of Missing Persons

  • Yoo, Ho Jin;Lee, Jiyeong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.37 no.5
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    • pp.293-302
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    • 2019
  • In mountain accident events, it is important for the search team commander to determine the search area in order to secure the Golden Time. Within this period, assistance and treatment to the concerned individual will most likely prevent further injuries and harm. This paper proposes a method to determine the search priority area based on missing persons behavior and missing persons incidents statistics. GIS (Geographic Information System) and MCDM (Multi Criteria Decision Making) are integrated by applying WLC (Weighted Linear Combination) techniques. Missing persons were classified into five types, and their behavioral characteristics were analyzed to extract seven geographic analysis factors. Next, index values were set up for each missing person and element according to the behavioral characteristics, and the raster data generated by multiplying the weight of each element are superimposed to define models to select search priority areas, where each weight is calculated from the AHP (Analytical Hierarchy Process) through a pairwise comparison method obtained from search operation experts. Finally, the model generated in this study was applied to a missing person case through a virtual missing scenario, the priority area was selected, and the behavioral characteristics and topographical characteristics of the missing persons were compared with the selected area. The resulting analysis results were verified by mountain rescue experts as 'appropriate' in terms of the behavior analysis, analysis factor extraction, experimental process, and results for the missing persons.

TANFIS Classifier Integrated Efficacious Aassistance System for Heart Disease Prediction using CNN-MDRP

  • Bhaskaru, O.;Sreedevi, M.
    • International Journal of Computer Science & Network Security
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    • v.22 no.10
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    • pp.171-176
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    • 2022
  • A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM2G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

Development of a Gas Sensor System with Built-in Low-power Signal Extraction Technique (저전력 신호 추출 기법이 내장된 가스 센서 시스템 개발)

  • Jang-Su Hyeon;Hyeon-June Kim
    • Journal of Sensor Science and Technology
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    • v.32 no.2
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    • pp.105-109
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    • 2023
  • In this study, we present a power-efficient driving method for gas sensor systems based on the analysis of input signal characteristics. The analysis of the gas sensor output signal characteristics in the frequency domain shows that most of the signal portions are distributed in a relatively low frequency region when extracting the gas sensor signal, which can lead to further performance improvement of the gas sensor system. Therefore, the proposed gas signal extracting technique changes the operating frequency of the read-out circuit based on the frequency characteristics of the output signal of the gas sensor, resulting in a reduction of power consumption at the whole system level. The proposed sensing technique, which can be applied to a general-purpose commercial gas sensor system, was implemented in a printed circuit board (PCB) to verify its effectiveness at the commercial level.

A Research of Trends in Development of Bio-Diesel Aviation Fuel Technology using Microalgae (미세조류 이용 바이오디젤 항공유 기술개발 동향 연구)

  • Han-Young Yoon
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.32 no.2
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    • pp.151-158
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    • 2024
  • Microalgae are aquatic microorganisms capable of photosynthetic growth using water, carbon dioxide and sunlight, and can replace petroleum for transportation. It is receiving great attention as a potential next-generation biological resource. The microalgae biodiesel production process is largely based on the development of highly efficient strains and mass production. It consists of cultivation, harvesting, oil extraction, fuel conversion and by-product utilization. Currently, microalgae diesel is 3-5 times more expensive than petroleum diesel. However, with the optimization of each element technology and the development of integrated systems, not only biofuels, but also industrial materials, wastewater treatment, and greenhouse gases As application expands to various fields such as abatement, the timing of commercialization may be brought forward. Oil prices have recently fallen due to the influence of sail gas. Although there has been a significant drop, global warming is an urgent challenge for current and future generations. In particular, Korea, which does not have oil resources, We must always prepare for political environmental changes, high oil prices, and energy crises. In this paper, the need for eco-friendly biofuel for carbon dioxide conversion. In addition to research trends, domestic and international research trends, and economic prospects, the concept of microalgae and the element technologies of the biodiesel production process are briefly discussed introduced.

A Study on the Integration of Airborne LiDAR and UAV Data for High-resolution Topographic Information Construction of Tidal Flat (갯벌지역 고해상도 지형정보 구축을 위한 항공 라이다와 UAV 데이터 통합 활용에 관한 연구)

  • Kim, Hye Jin;Lee, Jae Bin;Kim, Yong Il
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.38 no.4
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    • pp.345-352
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    • 2020
  • To preserve and restore tidal flats and prevent safety accidents, it is necessary to construct tidal flat topographic information including the exact location and shape of tidal creeks. In the tidal flats where the field surveying is difficult to apply, airborne LiDAR surveying can provide accurate terrain data for a wide area. On the other hand, we can economically obtain relatively high-resolution data from UAV (Unmanned Aerial Vehicle) surveying. In this study, we proposed the methodology to generate high-resolution topographic information of tidal flats effectively by integrating airborne LiDAR and UAV point clouds. For the purpose, automatic ICP (Iterative Closest Points) registration between two different datasets was conducted and tidal creeks were extracted by applying CSF (Cloth Simulation Filtering) algorithm. Then, we integrated high-density UAV data for tidal creeks and airborne LiDAR data for flat grounds. DEM (Digital Elevation Model) and tidal flat area and depth were generated from the integrated data to construct high-resolution topographic information for large-scale tidal flat map creation. As a result, UAV data was registered without GCP (Ground Control Point), and integrated data including detailed topographic information of tidal creeks with a relatively small data size was generated.

A Study of the DB Design Standard for Submitting Completion Drawings for Auto-Renewal of Underground Facility Information (지하시설물정보 자동갱신을 위한 준공도서 제출 표준DB 설계 연구)

  • Park, Dong Hyun;Jang, Yong Gu;Ryu, Ji Song
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.38 no.6
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    • pp.681-688
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    • 2020
  • The Under Space Integrated Map has been constructed consistently from '15 construction projects until the present time in an effort to implement the "ground sinking prevention method" for the purpose of strengthening underground safety management. The constructed Under Space Integrated Map is utilized to provide information to the person in charge at local government through application of the system of underground information based on the administrative network and to deliver this to specialized underground-safety-effects -evaluation organizations through map extraction based on a floor plan. It suffers from a limitation in its practical use, however, since information is only provided, without promoting a separate renewal project. Although in Section 1 of Article 42 in the Special Law Concerning Underground Safety Management the content pertaining to submission obligations of completion drawings related to underground information including change and renewal are stated explicitly in order to solve this problem, submission is not sufficient since a submission window based only on the administrative network is operated. Accordingly, the Ministry of Land, Infrastructure, and Transport constructed an online system for submitting completion drawings, in an attempt to change the method by which entities involved in underground development directly submitted completion drawings. In this study, a DB standard relating to submitting completion drawings was designed and applied in order to construct an auto-renewal system based on submitted completion drawings, which will be extended to cover the range to underground structures hereafter.