<title>Fast disparity estimation algorithm for mesh-based stereo image/video compression with two-stage hybrid approach</title>

Efficient stereo video coding system for immersive teleconference with two-stage hybrid disparity estimation algorithm

Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429) ◽

10.1109/icip.2003.1247070 ◽

2004 ◽

Cited By ~ 1

Author(s):

Shao-Yi Chien ◽

Shu-Han Yu ◽

Li-Fu Ding ◽

Yun-Nien Huang ◽

Liang-Gee Chen

Keyword(s):

Video Coding ◽

Estimation Algorithm ◽

Disparity Estimation ◽

Coding System ◽

Two Stage ◽

Stereo Video

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Fast disparity estimation algorithm based on features of disparity vector

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.01856 ◽

2013 ◽

Vol 32 (6) ◽

pp. 1856-1859

Author(s):

Xiao-wei SONG ◽

Lei YANG ◽

Zhong LIU ◽

Liang LIAO

Keyword(s):

Estimation Algorithm ◽

Disparity Estimation ◽

Disparity Vector

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Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers

Energies ◽

10.3390/en14082181 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2181

Author(s):

Rafik Nafkha ◽

Tomasz Ząbkowski ◽

Krzysztof Gajowniczek

Keyword(s):

Deep Learning ◽

Power Grid ◽

Hybrid Approach ◽

Electricity Consumption ◽

Maximum Power ◽

Learning Approaches ◽

Two Stage ◽

Load Optimization ◽

Optimal Capacity ◽

Maximal Load

The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.

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MultiStep Ahead Forecasting for Hourly PM10 and PM2.5 Based on Two-Stage Decomposition Embedded Sample Entropy and Group Teacher Optimization Algorithm

Atmosphere ◽

10.3390/atmos12010064 ◽

2021 ◽

Vol 12 (1) ◽

pp. 64

Author(s):

Feng Jiang ◽

Yaqian Qiao ◽

Xuchu Jiang ◽

Tianhai Tian

Keyword(s):

Optimization Algorithm ◽

Hybrid Approach ◽

Forecast Accuracy ◽

Ensemble Empirical Mode Decomposition ◽

Sample Entropy ◽

Air Pollutant ◽

Intrinsic Mode Functions ◽

Concentration Data ◽

Two Stage ◽

Pm10 And Pm2.5

The randomness, nonstationarity and irregularity of air pollutant data bring difficulties to forecasting. To improve the forecast accuracy, we propose a novel hybrid approach based on two-stage decomposition embedded sample entropy, group teaching optimization algorithm (GTOA), and extreme learning machine (ELM) to forecast the concentration of particulate matter (PM10 and PM2.5). First, the improvement complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed to decompose the concentration data of PM10 and PM2.5 into a set of intrinsic mode functions (IMFs) with different frequencies. In addition, wavelet transform (WT) is utilized to decompose the IMFs with high frequency based on sample entropy values. Then the GTOA algorithm is used to optimize ELM. Furthermore, the GTOA-ELM is utilized to predict all the subseries. The final forecast result is obtained by ensemble of the forecast results of all subseries. To further prove the predictable performance of the hybrid approach on air pollutants, the hourly concentration data of PM2.5 and PM10 are used to make one-step-, two-step- and three-step-ahead predictions. The empirical results demonstrate that the hybrid ICEEMDAN-WT-GTOA-ELM approach has superior forecasting performance and stability over other methods. This novel method also provides an effective and efficient approach to make predictions for nonlinear, nonstationary and irregular data.

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