• 제목/요약/키워드: Prediction-Based

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H.264 기반 스케일러블 비디오 부호화에서 부호화 효율을 고려한 잔여신호 예측에 관한 연구 (Adaptive Residual Prediction for coding efficiency on H.264 Based Scalable Video Coding)

  • 박성호;오형석;김원하
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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    • pp.189-191
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    • 2005
  • In the scalable extension of H.264/AVC, the codec is based on a layered approach to enable spatial scalability. In each layer, the basic concepts of motion compensated prediction and intra prediction are employed as in standard H.264/AVC. Additionally inter-layer prediction algorithm between successive spatial layers is applied to remove redundancy. In the inter-layer prediction, as the prediction we can use the signal that is the upsampled signal of the lower resolution layer. In this case, coding efficiency can be variable as the kinds of interpolation filter. In this paper, we investigate the approach to select the interpolation filter for residual signal in order to optimal prediction.

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Landslide Stability Analysis and Prediction Modeling with Landslide Occurrences on KOMPSAT EOC Imagery

  • Chi, Kwang-Hoon;Lee, Ki-Won;Park, No-Wook
    • 대한원격탐사학회지
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    • 제18권1호
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    • pp.1-12
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    • 2002
  • Landslide prediction modeling has been regarded as one of the important environmental applications in GIS. While, landslide stability in a certain area as collateral process for prediction modeling can be characterized by DEM-based hydrological features such as flow-direction, flow-accumulation, flow-length, wetness index, and so forth. In this study, Slope-Area plot methodology followed by stability index mapping with these hydrological variables is firstly performed for stability analysis with actual landslide occurrences at Boeun area, Korea, and then Landslide prediction modeling based on likelihood ratio model for landslide potential mapping is carried out; in addition, KOMPSAT EOC imagery is used to detect the locations and scalped scale of Landslide occurrences. These two tasks are independently processed for preparation of unbiased criteria, and then results of those are qualitatively compared. As results of this case study, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, the results in the form of land stability map by landslide prediction model are not fully matched with those of hydrologic landslide analysis due to the heuristic scheme based on location of existed landslide occurrences within prediction approach, especially zones of not-investigated occurrences. Therefore, it is expected that the resets on the space-robustness of landslide prediction models in conjunction with DEM-based landslide stability analysis can be effectively utilized to search out unrevealed or hidden landslide occurrences.

다중 모델을 이용한 완전연결 신경망 기반 화면내 예측 (Intra Prediction Using Multiple Models Based on Fully Connected Neural Network)

  • 문기화;박도현;김민재;권형진;김재곤
    • 방송공학회논문지
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    • 제26권6호
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    • pp.758-765
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    • 2021
  • 최근 딥러닝 기술을 비디오 부호화에 적용하는 다양한 연구가 진행되고 있다. 본 논문은 차세대 비디오 코덱인 VVC(Versatile Video Coding)에 채택된 신경망 기반의 기술인 MIP(Matrix-based Intra Prediction)를 확장한 완전연결계층(Fully Connected Layer) 기반의 다중 모델을 이용하는 화면내 예측 부호화 기법을 제시한다. 또한 다중 화면내 예측 모델을 위한 효율적인 학습기법을 제안한다. HEVC(High Efficiency Video Coding)에서의 성능검증을 위해 VVC의 MIP와 제안하는 완전연결계층 기반 다중 화면내 예측 모델을 HEVC의 참조 소프트웨어인 HM16.19에 추가적인 화면내 예측모드로 구현하였다. 실험결과 제안하는 방법이 HM16.19와 VVC MIP 대비 각각 0.47%과 0.19% BD-rate 성능향상이 있음을 확인하였다.

선박 이동 경로 예측을 위한 해상 영역 분할 및 영역 단위 목적지 예측 방법 (Maritime region segmentation and segment-based destination prediction methods for vessel path prediction)

  • 김종희;정찬호;강도근;이창진
    • 전기전자학회논문지
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    • 제24권2호
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    • pp.661-664
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    • 2020
  • 본 논문에서 우리는 선박의 이동 경로를 예측하기 위하여, 해상 영역을 분할하고, 분할된 영역을 기반으로 선박의 목적지를 예측하는 방법을 제안한다. 해상 영역을 분할하기 위하여 과거 이동 경로를 토대로 생성된 목적지 후보들을 군집화한다. 그리고, 선박이 이동할 목적지 영역을 예측하기 위해서 현재 위치에서 주어진 경로의 선형 여부와 향후 예측 시간에 따른 불확실성에 따라 다른 예측 방법을 적용한다. 예측에 사용하는 방법에는 선형 영역에서는 등속 운동을 가정한 선형 예측 방법, 불확실성이 높은 비선형 영역에서는 과거 경로 중 유사한 경로와 비슷한 움직임을 보일 것이라고 가정한 유사 경로 이용 예측 방법을 사용한다. 실험 결과에서 해당 방법이 선형 예측, 유사 경로 이용 예측 방법을 단독으로 적용하는 것에 비해 더 우수함을 보인다.

Analysis Model Evaluation based on IoT Data and Machine Learning Algorithm for Prediction of Acer Mono Sap Liquid Water

  • Lee, Han Sung;Jung, Se Hoon
    • 한국멀티미디어학회논문지
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    • 제23권10호
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    • pp.1286-1295
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    • 2020
  • It has been increasingly difficult to predict the amounts of Acer mono sap to be collected due to droughts and cold waves caused by recent climate changes with few studies conducted on the prediction of its collection volume. This study thus set out to propose a Big Data prediction system based on meteorological information for the collection of Acer mono sap. The proposed system would analyze collected data and provide managers with a statistical chart of prediction values regarding climate factors to affect the amounts of Acer mono sap to be collected, thus enabling efficient work. It was designed based on Hadoop for data collection, treatment and analysis. The study also analyzed and proposed an optimal prediction model for climate conditions to influence the volume of Acer mono sap to be collected by applying a multiple regression analysis model based on Hadoop and Mahout.

Prediction-based Dynamic Thread Pool System for Massively Multi-player Online Game Server

  • Ju, Woo-Suk;Im, Choong-Jae
    • 한국멀티미디어학회논문지
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    • 제12권6호
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    • pp.876-881
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    • 2009
  • Online game servers usually has been using the static thread pool system. But this system is not fit for huge online game server because the overhead is always up-and-down. Therefore, in this paper, we suggest the new algorithm for huge online game server. This algorithm is based on the prediction-based dynamic thread pool system. But it was developed for web servers and every 0.1 seconds the system prediction the needed numbers of threads and determine the thread pool size. Some experimental results show that the check time of 0.4 seconds is the best one for online game server and if the number of worker threads do not excess or lack to the given threshold then we do not predict and keep the current state. Otherwise we apply the prediction algorithm and change the number of threads. Some experimental results shows that this proposed algorithm reduce the overhead massively and make the performance of huge online game server improved in comparison to the static thread pool system.

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Bayesian Typhoon Track Prediction Using Wind Vector Data

  • Han, Minkyu;Lee, Jaeyong
    • Communications for Statistical Applications and Methods
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    • 제22권3호
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    • pp.241-253
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    • 2015
  • In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.

Defect Severity-based Defect Prediction Model using CL

  • Lee, Na-Young;Kwon, Ki-Tae
    • 한국컴퓨터정보학회논문지
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    • 제23권9호
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    • pp.81-86
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    • 2018
  • Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.

필드데이터 기반의 유도탄 신뢰도 예측 (Reliability Prediction Based on Field Failure Data of Guided Missile)

  • 서양우;이계신;이연호;김제용
    • 한국신뢰성학회지:신뢰성응용연구
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    • 제18권3호
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    • pp.250-259
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    • 2018
  • Purpose: Previously, missile reliability prediction is based on theoretical failure prediction model. It has shown that the predicted reliability is inadequate to real field data. Although an MTTF based reliability prediction method using real field data has recently been studied to overcome this issue. In this paper, we present a more realistic method, considering MTBF concept, to predict missile reliability. Methods: In this paper we proposed a modified survival model. This model is considering MTBF as its core concept, and failed missiles in the model are to be repaired and redeployed. We compared the modified model (MTBF) and the previous model (MTTF) in terms of fitness against the real failure data. Results: The reliability prediction result of MTBF based model is closer to fields failure data set than that of MTTF based model. Conclusion: The proposed MTBF concept is more fitted to real failure data of missile than MTTF concept. The methodology of this study can be applied to analyze field failure data of other similar missiles.

A Study on the Field Strength Prediction of a Ground-wave Based Time Broadcasting Transmitter Station in the Korean Peninsula

  • Lee, Sun Yong;Choi, Yun Sub;Hwang, Sang-Wook;Yang, Sung-Hoon;Lee, Chang-Bok;Lee, Sang Jeong
    • Journal of Positioning, Navigation, and Timing
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    • 제3권2호
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    • pp.83-90
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    • 2014
  • In this study, to improve an existing ground-wave based time broadcasting system, a study that predicts the field distribution and field strength of the transmitted signal of a new ground-wave based time broadcasting system was performed. The prediction area was assumed to be the Korean peninsula; and to reflect the mountainous terrain of the Korean peninsula in the prediction of the variations of field distribution and field strength, a new prediction method based on the Monteath model was proposed and utilized. As field distribution changes depending on the position of a transmitter station, potential sites for the transmitter station were selected considering the geographical characteristics. In this regard, the ground conductivity information of North Korea cannot be obtained, and thus, the ground conductivity of the North Korean region was reflected considering the geological characteristics of South Korea and North Korea. Based on this, the variations of field distribution and field strength were predicted by setting the Korean peninsula as the prediction area, and the prediction results depending on the position of the transmitter station were discussed.