• Title/Summary/Keyword: auto-context

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Scale Invariant Auto-context for Object Segmentation and Labeling

  • Ji, Hongwei;He, Jiangping;Yang, Xin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.8
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    • pp.2881-2894
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    • 2014
  • In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

Study on the Impact of Joining the CPTPP on the Korean Auto Industry (CPTPP 가입이 국내 자동차산업에 미치는 영향 연구)

  • Jung-Ran Cho
    • Korea Trade Review
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    • v.45 no.1
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    • pp.137-153
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    • 2020
  • On February 14, 2019, the government of Korea formally decided to consider the feasibility of joining the Comprehensive and Progressive Trans-Pacific Partnership (CPTPP) and has since been conducting bilateral consultations with individual member countries. In terms of the impact estimation, the CPTPP is actually a Korea-Japan FTA, and the most sensitive issue in the FTA is the opening of the auto industry market to Japan. Despite these circumstances, previous studies have predicted that the auto industry will be a beneficiary industry when joining the CPTPP. However, the Korean auto industry is opposed to joining the CPTPP. In order to investigate the cause of this discrepancy, this paper examines the problems of previous studies in estimating the impact of joining the CPTPP and found that the preceding study did not consider the industrial characteristics of the auto sector, especially in the context of Japan-Korea trade, and was heavily dependent on the Armington elasticity (structure) in the demand function of the GTAP CGE model. As a result, the domestic auto sector could lower prices and increase exports when joining the CPTPP. This paper attempts to precisely re-estimate the impact of joining the CPTPP on the auto sector in a way that corrects these problems by changing the CGE model and reflecting the major characteristics of the industry, with policy implications for the negotiation of CPTPP accession.

AUTO-CORRELATIONS AND BOUNDS ON THE NONLINEARITY OF VECTOR BOOLEAN FUNCTIONS

  • Kim, Wansoon;Park, Junseok
    • Journal of the Chungcheong Mathematical Society
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    • v.17 no.1
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    • pp.47-56
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    • 2004
  • The nonlinearity of a Boolean function f on $GF(2)^n$ is the minimum hamming distance between f and all affine functions on $GF(2)^n$ and it measures the ability of a cryptographic system using the functions to resist against being expressed as a set of linear equations. Finding out the exact value of the nonlinearity of given Boolean functions is not an easy problem therefore one wants to estimate the nonlinearity using extra information on given functions, or wants to find a lower bound or an upper bound on the nonlinearity. In this paper we extend the notion of auto-correlations of Boolean functions to vector Boolean functions and obtain upper bounds and a lower bound on the nonlinearity of vector Boolean functions in the context of their auto-correlations. Also we can describe avalanche characteristics of vector Boolean functions by examining the extended notion of auto-correlations.

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Tandem High-dose Chemotherapy and Autologous Stem Cell Transplantation in Children with Brain Tumors : Review of Single Center Experience

  • Sung, Ki Woong;Lim, Do Hoon;Shin, Hyung Jin
    • Journal of Korean Neurosurgical Society
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    • v.61 no.3
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    • pp.393-401
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    • 2018
  • The prognosis of brain tumors in children has improved for last a few decades. However, the prognosis remains dismal in patients with recurrent brain tumors. The outcome for infants and young children in whom the use of radiotherapy (RT) is very limited because of unacceptable long-term adverse effect of RT remains poor. The prognosis is also not satisfactory when a large residual tumor remains after surgery or when leptomeningeal seeding is present at diagnosis. In this context, a strategy using high-dose chemotherapy and autologous stem cell transplantation (HDCT/auto-SCT) has been explored to improve the prognosis of recurrent or high-risk brain tumors. This strategy is based on the hypothesis that chemotherapy dose escalation might result in improvement in survival rates. Recently, the efficacy of tandem HDCT/auto-SCT has been evaluated in further improving the outcome. This strategy is based on the hypothesis that further dose escalation might result in further improvement in survival rates. At present, the number of studies employing tandem HDCT/auto-SCT for brain tumors is limited. However, results of these pilot studies suggest that tandem HDCT/auto-SCT may further improve the outcome. In this review, we will summarize our single center experience with tandem HDCT/auto-SCT for recurrent or high-risk brain tumors.

An Auto Playlist Generation System with One Seed Song

  • Bang, Sung-Woo;Jung, Hye-Wuk;Kim, Jae-Kwang;Lee, Jee-Hyong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.10 no.1
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    • pp.19-24
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    • 2010
  • The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users have a tendency to build playlist for manage songs. However the manual selection of songs for creating playlist is a troublesome work. This paper proposes an auto playlist generation system considering user context of use and preferences. This system has two separated systems; 1) the mood and emotion classification system and 2) the music recommendation system. Firstly, users need to choose just one seed song for reflecting their context of use. Then system recommends candidate song list before the current song ends in order to fill up user playlist. User also can remove unsatisfied songs from the recommended song list to adapt the user preference model on the system for the next song list. The generated playlists show well defined mood and emotion of music and provide songs that the preference of the current user is reflected.

A Deep Learning-based Streetscapes Safety Score Prediction Model using Environmental Context from Big Data (빅데이터로부터 추출된 주변 환경 컨텍스트를 반영한 딥러닝 기반 거리 안전도 점수 예측 모델)

  • Lee, Gi-In;Kang, Hang-Bong
    • Journal of Korea Multimedia Society
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    • v.20 no.8
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    • pp.1282-1290
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    • 2017
  • Since the mitigation of fear of crime significantly enhances the consumptions in a city, studies focusing on urban safety analysis have received much attention as means of revitalizing the local economy. In addition, with the development of computer vision and machine learning technologies, efficient and automated analysis methods have been developed. Previous studies have used global features to predict the safety of cities, yet this method has limited ability in accurately predicting abstract information such as safety assessments. Therefore we used a Convolutional Context Neural Network (CCNN) that considered "context" as a decision criterion to accurately predict safety of cities. CCNN model is constructed by combining a stacked auto encoder with a fully connected network to find the context and use it in the CNN model to predict the score. We analyzed the RMSE and correlation of SVR, Alexnet, and Sharing models to compare with the performance of CCNN model. Our results indicate that our model has much better RMSE and Pearson/Spearman correlation coefficient.

Development of facial recognition application for automation logging of emotion log (감정로그 자동화 기록을 위한 표정인식 어플리케이션 개발)

  • Shin, Seong-Yoon;Kang, Sun-Kyoung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.4
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    • pp.737-743
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    • 2017
  • The intelligent life-log system proposed in this paper is intended to identify and record a myriad of everyday life information as to the occurrence of various events based on when, where, with whom, what and how, that is, a wide variety of contextual information involving person, scene, ages, emotion, relation, state, location, moving route, etc. with a unique tag on each piece of such information and to allow users to get a quick and easy access to such information. Context awareness generates and classifies information on a tag unit basis using the auto-tagging technology and biometrics recognition technology and builds a situation information database. In this paper, we developed an active modeling method and an application that recognizes expressionless and smile expressions using lip lines to automatically record emotion information.

MEMBERSHIP FUNCTION TUNING OF FUZZY NEURAL NETWORKS BY IMMUNE ALGORITHM

  • Kim, Dong-Hwa
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.3
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    • pp.261-268
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    • 2002
  • This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

Development of camera auto-tracking system for telemanipulators (원격조작 로보트를 위한 카메라 추종시스템 개발)

  • 박영수;윤지섭;엄태준;이재설
    • 제어로봇시스템학회:학술대회논문집
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    • 1990.10a
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    • pp.825-830
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    • 1990
  • This paper reports the design procedure and testing result of a servo driven pan/tilt device which is capable of tracking arbitrary movement of a specified target object. In order to achieve real-time acquisition of feedback signal, a 2 degrees-of-freedom non-contact type displacement follower is used. The performance of the system is tested for different target velocities and control gains. The result of the research may provide an effective tool for visual transfer in the context of teleoperation.

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An Auto-blogging System based Context Model for Micro-blogging Service (마이크로 블로깅 서비스를 지원하기 위한 컨텍스트 모델 기반 자동 블로깅 시스템)

  • Park, Jae-Min;Lee, Sang-Yong
    • Journal of Digital Convergence
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    • v.10 no.4
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    • pp.341-346
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    • 2012
  • Social network service is service that enables the human network to be built up on web. It is important to record users' information simply and establish the network with people based on the information to provide with the social network service effectively. But it is very troublesome work for the user to input his or her own information on the mobile environment. In this paper we suggested a system which classifies users' behavior using context and creates blogging sentences automatically after inferring the destination. For this, users' behavior is classified and the destination is inferred with the sequence matching method using Naive Bayes classification. Then sentences which are suitable for situation is created by arranging the processed context using the structure of 5W1H. The system was evaluated satisfaction degree by comparing the created sentences based on actually collected data with users' intension and got accuracy rate of 88.73%.