• 제목/요약/키워드: Absolute error

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정합 오차 기준을 확장한 제한된 1비트 변환 알고리즘 기반의 움직임 예측 (Constrained One-Bit Transform based Motion Estimation using Extension of Matching Error Criterion)

  • 이상구;정제창
    • 방송공학회논문지
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    • 제18권5호
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    • pp.730-737
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    • 2013
  • 본 논문은 정합 오차 기준을 확장한 제한된 1비트 변환 (Constrained One-Bit Transform : C1BT) 기반의 움직임 예측 알고리즘을 제안하였다. 제한된 1비트 변환 기반의 움직임 예측 알고리즘에서는 정합 오차 기준으로 기존의 움직임 예측 방법인 전역 탐색 알고리즘 (Full Search Algorithm: FSA)에서 사용되는 SAD (Sum of Absolute Differences) 대신 NNMP (Number of Non-Matching Points)를 사용하여 하드웨어 구현을 용이하게 하고 연산량을 크게 줄였으나 움직임 예측의 정확도를 감소시켰다. 이 점을 개선하고자 이 논문에서는 제한된 1비트 변환의 정합 오차 기준을 확장하여 움직임 예측의 정확도를 높이는 알고리즘을 제안하였고 이는 기존의 알고리즘과 비교한 결과 PSNR (Peak Signal to Nosie Ratio) 측면에서 더 우수한 성능을 보였다.

Modeling and Forecasting Saudi Stock Market Volatility Using Wavelet Methods

  • ALSHAMMARI, Tariq S.;ISMAIL, Mohd T.;AL-WADI, Sadam;SALEH, Mohammad H.;JABER, Jamil J.
    • The Journal of Asian Finance, Economics and Business
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    • 제7권11호
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    • pp.83-93
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    • 2020
  • This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

Hourly Steel Industry Energy Consumption Prediction Using Machine Learning Algorithms

  • Sathishkumar, VE;Lee, Myeong-Bae;Lim, Jong-Hyun;Shin, Chang-Sun;Park, Chang-Woo;Cho, Yong Yun
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2019년도 추계학술발표대회
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    • pp.585-588
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    • 2019
  • Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

Exploiting Neural Network for Temporal Multi-variate Air Quality and Pollutant Prediction

  • Khan, Muneeb A.;Kim, Hyun-chul;Park, Heemin
    • 한국멀티미디어학회논문지
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    • 제25권2호
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    • pp.440-449
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    • 2022
  • In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

Feasibility study of using triple-energy CT images for improving stopping power estimation

  • Yejin Kim;Jin Sung Kim ;Seungryong Cho
    • Nuclear Engineering and Technology
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    • 제55권4호
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    • pp.1342-1349
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    • 2023
  • The planning accuracy of charged particle therapy (CPT) is subject to the accuracy of stopping power (SP) estimation. In this study, we propose a method of deriving a pseudo-triple-energy CT (pTECT) that can be achievable in the existing dual-energy CT (DECT) systems for better SP estimation. In order to remove the direct effect of errors in CT values, relative CT values according to three scanning voltage settings were used. CT values of each tissue substitute phantom were measured to show the non-linearity of the values thereby suggesting the absolute difference and ratio of CT values as parameters for SP estimation. Electron density, effective atomic number (EAN), mean excitation energy and SP were calculated based on these parameters. Two of conventional methods were implemented and compared to the proposed pTECT method in terms of residuals, absolute error and root-mean-square-error (RMSE). The proposed method outperformed the comparison methods in every evaluation metrics. Especially, the estimation error for EAN and mean excitation using pTECT were converging to zero. In this proof-of-concept study, we showed the feasibility of using three CT values for accurate SP estimation. Our suggested pTECT method indicates potential clinical utility of spectral CT imaging for CPT planning.

Designing of the Beheshtabad water transmission tunnel based on the hybrid empirical method

  • Mohammad Rezaei;Hazhar Habibi
    • Structural Engineering and Mechanics
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    • 제86권5호
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    • pp.621-633
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    • 2023
  • Stability analysis and support system estimation of the Beheshtabad water transmission tunnel is investigated in this research. A combination approach based on the rock mass rating (RMR) and rock mass quality index (Q) is used for this purpose. In the first step, 40 datasets related to the petrological, structural, hydrological, physical, and mechanical properties of tunnel host rocks are measured in the field and laboratory. Then, RMR, Q, and height of influenced zone above the tunnel roof are computed and sorted into five general groups to analyze the tunnel stability and determine its support system. Accordingly, tunnel stand-up time, rock load, and required support system are estimated for five sorted rock groups. In addition, various empirical relations between RMR and Q i.e., linear, exponential, logarithmic, and power functions are developed using the analysis of variance (ANOVA). Based on the significance level (sig.), determination coefficient (R2) and Fisher-test (F) indices, power and logarithmic equations are proposed as the optimum relations between RMR and Q. To validate the proposed relations, their results are compared with the results of previous similar equations by using the variance account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) indices. Comparison results showed that the accuracy of proposed RMR-Q relations is better than the previous similar relations and their outputs are more consistent with actual data. Therefore, they can be practically utilized in designing the tunneling projects with an acceptable level of accuracy and reliability.

New mathematical approach to determine solar radiation for the southwestern coastline of Pakistan

  • Atteeq Razzak;Zaheer Uddin;M. Jawed Iqbal
    • Advances in Energy Research
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    • 제8권2호
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    • pp.111-123
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    • 2022
  • Solar Energy is the energy of solar radiation carried by them in the form of heat and light. It can be converted into electricity. Solar potential depends on the site's atmosphere; the solar energy distribution depends on many factors, e.g., turbidity, cloud types, pollution levels, solar altitude, etc. We estimated solar radiation with the help of the Ashrae clear-sky model for three locations in Pakistan, namely Pasni, Gwadar, and Jiwani. As these locations are close to each other as compared to the distance between the sun and earth, therefore a slight change of latitude and longitude does not make any difference in the calculation of direct beam solar radiation (BSR), diffuse solar radiation (DSR), and global solar radiation (GSR). A modified formula for declination angle is also developed and presented. We also created two different models for Ashrae constants. The values of these constants are compared with the standard Ashrae Model. A good agreement is observed when we used these constants to calculate BSR, DSR, GSR, the Root mean square error (RMSE), Mean Absolute error (MABE), Mean Absolute percent error (MAPE), and chisquare (χ2) values are in acceptance range, indicating the validity of the models.

지상기반 라이다의 측정 오차에 영향을 미치는 요인 분석 (Analysis of Factors Influencing the Measurement Error of Ground-based LiDAR)

  • 강동범;허종철;고경남
    • 한국태양에너지학회 논문집
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    • 제37권6호
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    • pp.25-37
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    • 2017
  • A study on factors influencing measurement error of Ground-based LiDAR(Light Detection And Ranging) system was conducted in Kimnyeong wind turbine test site on Jeju Island. Three properties of wind including inclined angle, turbulence intensity and power law exponent were taken into account as factors influencing the measurement error of Ground-based LiDAR. In order to calculate LiDAR measurements error, 2.5-month wind speed data collected from LiDAR (WindCube v2) were compared with concurrent data from the anemometer on a nearby 120m-high meteorological mast. In addition, data filtering was performed and its filtering criteria was based on the findings at previous researches. As a result, at 100m above ground level, absolute LiDAR error rate with absolute inclined angle showed 4.58~13.40% and 0.77 of the coefficients of determination, $R^2$. That with turbulence intensity showed 3.58~23.94% and 0.93 of $R^2$ while that with power law exponent showed 4.71~9.53% and 0.41 of $R^2$. Therefore, it was confirmed that the LiDAR measurement error was highly affected by inclined angle and turbulence intensity, while that did not much depend on power law exponent.

카세그레인 망원경의 볼록비구면 반사경 파면오차 측정 (Testing of a Convex Aspheric Secondary Mirror for the Cassegrain Telescope)

  • 김고은;이혁교;양호순
    • 한국광학회지
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    • 제28권6호
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    • pp.290-294
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    • 2017
  • 카세그레인 망원경은 오목한 주경과 볼록한 부경으로 이루어져있다. 특히 부경의 경우 크기는 작지만 볼록한 형태로 빛을 모두 퍼트려 파면오차 측정이 어렵다. 본 논문에서는 비구면 계수가 여러 개인 볼록비구면 반사경의 파면오차를 Simpson-Oland-Meckel (SOM) 힌들 테스트를 적용하여 측정하였다. 그리고 실험 구성에서 발생하는 계통오차를 분리해내기 위해 QN 절대측정법을 추가로 적용함으로써 힌들 렌즈 제작 및 정렬 오차를 포함한 계통오차를 보정하고 볼록비구면 반사경만의 파면오차를 구하였다. 이렇게 구한 볼록비구면 반사경의 파면오차와 QED사의 ASI (Aspheric Stitching Interferometer)로 측정한 파면오차와 비교한 결과, 모두 $45^{\circ}$ 방향의 비점수차 형태를 가지며 rms 값의 차이가 약 2.5 nm rms 이내로 매우 작음을 확인하였다.

연속된 블록 오류 은닉을 위한 계층 탐색 기반의 고속 알고리즘 (Hierarchical Search-based Fast Schemes for Consecutive Block Error Concealment)

  • 전수열;손채봉;오승준;안창범
    • 방송공학회논문지
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    • 제9권4호
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    • pp.446-454
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    • 2004
  • 멀티미디어 시스템이 발전함에 따라 멀티미디어 서비스 내에서 영상데이터 압축의 중요성은 점점 강조되고 있다. 그러나 영상을 압축한 비트스트림에 오류가 발생할 경우 영상을 복원할 때 심각한 왜곡이 발생하고, 이 때문에 멀티미디어 서비스에서 오류 은닉 방법은 더욱 중요한 기술로 대두되고 있다. 이러한 점을 해결하기 위해 Hsia는 1차원 경계면에서의 정합벡터를 이용하여 손상된 블록에 발생한 오류를 복구하는 오류 은닉 방법을 제안하였다. 그러나 정합벡터를 구하기 위해서 손상된 블록을 중심으로 상위와 하위에 있는 블록의 경계면에 있는 모든 픽셀에 대한 MAD (Mean Absolute Difference)값을 계산해야 하기 때문에 이 방법은 많은 연산량이 필요하다. 많은 연산량을 해결하기 위해서 본 논문에서는 계층 탐색 기반의 고속 오류 은닉 방법을 제안한다. 제안하는 방법에서는 정합벡터를 찾기 위한 확인점을 줄여 계산량을 감소시킨다. 제안한 방법을 Hsia가 제안한 방법과 비교하였을 때 화질을 유지하면서 연산량을 약 3배 줄일 수 있었다.