The Experts below are selected from a list of 40134 Experts worldwide ranked by ideXlab platform
Fan Zhang - One of the best experts on this subject based on the ideXlab platform.
-
a novel bias correction framework of tmpa 3b42 daily precipitation data using Similarity Matrix homogeneous conditions
Science of The Total Environment, 2019Co-Authors: Bahram Choubin, Ashok K. Mishra, Massoud Goodarzi, Shahaboddin Shamshirband, Esmatullah Ghaljaee, Shahram Khalighisigaroodi, Fan ZhangAbstract:Abstract Reduction of bias in remotely sensed precipitation products is a major challenge in environment modeling, hydrology, and managing the water resources. Various bias correction techniques are applied to reduce the bias from pixel to gauge data. However, a successful methodology to improve bias correction on the daily scale is often challenging and limited. We present a methodology that can be used to correct the daily bias in remote sensing rainfall data, and to demonstrate the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 data was used. The proposed bias correction method is based on the concept of Similarity (homogeneous) conditions developed based on the periodicity and different percentile-based precipitation amount, and by identifying the best donor pixel to transfer bias correction factor to a specific ungauged pixel (the receptor pixel) based on the Similarity (elevation, latitude, and longitude). Bias correction factors were obtained using the mean bias-removal (MBR) and multiplicative ratio (MR) techniques in the cells of the Similarity Matrix. The proposed methodology demonstrates a significant removal of bias associated with TMPA 3B42 data sets and it is capable of removing the bias in daily precipitation data on an average by 57% (51%) in the gauged pixels, and 25% (22%) in the ungauged pixels for MBR (MR) method.
-
A novel bias correction framework of TMPA 3B42 daily precipitation data using Similarity Matrix/homogeneous conditions.
The Science of the total environment, 2019Co-Authors: Bahram Choubin, Shahram Khalighi-sigaroodi, Ashok K. Mishra, Massoud Goodarzi, Shahaboddin Shamshirband, Esmatullah Ghaljaee, Fan ZhangAbstract:Abstract Reduction of bias in remotely sensed precipitation products is a major challenge in environment modeling, hydrology, and managing the water resources. Various bias correction techniques are applied to reduce the bias from pixel to gauge data. However, a successful methodology to improve bias correction on the daily scale is often challenging and limited. We present a methodology that can be used to correct the daily bias in remote sensing rainfall data, and to demonstrate the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 data was used. The proposed bias correction method is based on the concept of Similarity (homogeneous) conditions developed based on the periodicity and different percentile-based precipitation amount, and by identifying the best donor pixel to transfer bias correction factor to a specific ungauged pixel (the receptor pixel) based on the Similarity (elevation, latitude, and longitude). Bias correction factors were obtained using the mean bias-removal (MBR) and multiplicative ratio (MR) techniques in the cells of the Similarity Matrix. The proposed methodology demonstrates a significant removal of bias associated with TMPA 3B42 data sets and it is capable of removing the bias in daily precipitation data on an average by 57% (51%) in the gauged pixels, and 25% (22%) in the ungauged pixels for MBR (MR) method.
Sang Keun Choe - One of the best experts on this subject based on the ideXlab platform.
-
Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Sungkyun Chang, Sang Keun ChoeAbstract:In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-Similarity Matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output-the cover-probabilities of one song to all other candidate songs-as a new representation vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the representation vectors; 2. the cosine distance between the representation vectors; and 3. the correlation between the vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.
-
ICASSP - Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Sungkyun Chang, Sang Keun ChoeAbstract:In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-Similarity Matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output–the cover-probabilities of one song to all other candidate songs–as a new representation vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the representation vectors; 2. the cosine distance between the representation vectors; and 3. the correlation between the vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.
Bahram Choubin - One of the best experts on this subject based on the ideXlab platform.
-
a novel bias correction framework of tmpa 3b42 daily precipitation data using Similarity Matrix homogeneous conditions
Science of The Total Environment, 2019Co-Authors: Bahram Choubin, Ashok K. Mishra, Massoud Goodarzi, Shahaboddin Shamshirband, Esmatullah Ghaljaee, Shahram Khalighisigaroodi, Fan ZhangAbstract:Abstract Reduction of bias in remotely sensed precipitation products is a major challenge in environment modeling, hydrology, and managing the water resources. Various bias correction techniques are applied to reduce the bias from pixel to gauge data. However, a successful methodology to improve bias correction on the daily scale is often challenging and limited. We present a methodology that can be used to correct the daily bias in remote sensing rainfall data, and to demonstrate the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 data was used. The proposed bias correction method is based on the concept of Similarity (homogeneous) conditions developed based on the periodicity and different percentile-based precipitation amount, and by identifying the best donor pixel to transfer bias correction factor to a specific ungauged pixel (the receptor pixel) based on the Similarity (elevation, latitude, and longitude). Bias correction factors were obtained using the mean bias-removal (MBR) and multiplicative ratio (MR) techniques in the cells of the Similarity Matrix. The proposed methodology demonstrates a significant removal of bias associated with TMPA 3B42 data sets and it is capable of removing the bias in daily precipitation data on an average by 57% (51%) in the gauged pixels, and 25% (22%) in the ungauged pixels for MBR (MR) method.
-
A novel bias correction framework of TMPA 3B42 daily precipitation data using Similarity Matrix/homogeneous conditions.
The Science of the total environment, 2019Co-Authors: Bahram Choubin, Shahram Khalighi-sigaroodi, Ashok K. Mishra, Massoud Goodarzi, Shahaboddin Shamshirband, Esmatullah Ghaljaee, Fan ZhangAbstract:Abstract Reduction of bias in remotely sensed precipitation products is a major challenge in environment modeling, hydrology, and managing the water resources. Various bias correction techniques are applied to reduce the bias from pixel to gauge data. However, a successful methodology to improve bias correction on the daily scale is often challenging and limited. We present a methodology that can be used to correct the daily bias in remote sensing rainfall data, and to demonstrate the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 data was used. The proposed bias correction method is based on the concept of Similarity (homogeneous) conditions developed based on the periodicity and different percentile-based precipitation amount, and by identifying the best donor pixel to transfer bias correction factor to a specific ungauged pixel (the receptor pixel) based on the Similarity (elevation, latitude, and longitude). Bias correction factors were obtained using the mean bias-removal (MBR) and multiplicative ratio (MR) techniques in the cells of the Similarity Matrix. The proposed methodology demonstrates a significant removal of bias associated with TMPA 3B42 data sets and it is capable of removing the bias in daily precipitation data on an average by 57% (51%) in the gauged pixels, and 25% (22%) in the ungauged pixels for MBR (MR) method.
Sungkyun Chang - One of the best experts on this subject based on the ideXlab platform.
-
Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Sungkyun Chang, Sang Keun ChoeAbstract:In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-Similarity Matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output-the cover-probabilities of one song to all other candidate songs-as a new representation vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the representation vectors; 2. the cosine distance between the representation vectors; and 3. the correlation between the vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.
-
ICASSP - Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Sungkyun Chang, Sang Keun ChoeAbstract:In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-Similarity Matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output–the cover-probabilities of one song to all other candidate songs–as a new representation vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the representation vectors; 2. the cosine distance between the representation vectors; and 3. the correlation between the vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.
Ashok K. Mishra - One of the best experts on this subject based on the ideXlab platform.
-
a novel bias correction framework of tmpa 3b42 daily precipitation data using Similarity Matrix homogeneous conditions
Science of The Total Environment, 2019Co-Authors: Bahram Choubin, Ashok K. Mishra, Massoud Goodarzi, Shahaboddin Shamshirband, Esmatullah Ghaljaee, Shahram Khalighisigaroodi, Fan ZhangAbstract:Abstract Reduction of bias in remotely sensed precipitation products is a major challenge in environment modeling, hydrology, and managing the water resources. Various bias correction techniques are applied to reduce the bias from pixel to gauge data. However, a successful methodology to improve bias correction on the daily scale is often challenging and limited. We present a methodology that can be used to correct the daily bias in remote sensing rainfall data, and to demonstrate the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 data was used. The proposed bias correction method is based on the concept of Similarity (homogeneous) conditions developed based on the periodicity and different percentile-based precipitation amount, and by identifying the best donor pixel to transfer bias correction factor to a specific ungauged pixel (the receptor pixel) based on the Similarity (elevation, latitude, and longitude). Bias correction factors were obtained using the mean bias-removal (MBR) and multiplicative ratio (MR) techniques in the cells of the Similarity Matrix. The proposed methodology demonstrates a significant removal of bias associated with TMPA 3B42 data sets and it is capable of removing the bias in daily precipitation data on an average by 57% (51%) in the gauged pixels, and 25% (22%) in the ungauged pixels for MBR (MR) method.
-
A novel bias correction framework of TMPA 3B42 daily precipitation data using Similarity Matrix/homogeneous conditions.
The Science of the total environment, 2019Co-Authors: Bahram Choubin, Shahram Khalighi-sigaroodi, Ashok K. Mishra, Massoud Goodarzi, Shahaboddin Shamshirband, Esmatullah Ghaljaee, Fan ZhangAbstract:Abstract Reduction of bias in remotely sensed precipitation products is a major challenge in environment modeling, hydrology, and managing the water resources. Various bias correction techniques are applied to reduce the bias from pixel to gauge data. However, a successful methodology to improve bias correction on the daily scale is often challenging and limited. We present a methodology that can be used to correct the daily bias in remote sensing rainfall data, and to demonstrate the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 data was used. The proposed bias correction method is based on the concept of Similarity (homogeneous) conditions developed based on the periodicity and different percentile-based precipitation amount, and by identifying the best donor pixel to transfer bias correction factor to a specific ungauged pixel (the receptor pixel) based on the Similarity (elevation, latitude, and longitude). Bias correction factors were obtained using the mean bias-removal (MBR) and multiplicative ratio (MR) techniques in the cells of the Similarity Matrix. The proposed methodology demonstrates a significant removal of bias associated with TMPA 3B42 data sets and it is capable of removing the bias in daily precipitation data on an average by 57% (51%) in the gauged pixels, and 25% (22%) in the ungauged pixels for MBR (MR) method.
