In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in...In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.展开更多
A new numerical differentiation method with local opti- mum by data segmentation is proposed. The segmentation of data is based on the second derivatives computed by a Fourier devel- opment method. A filtering process...A new numerical differentiation method with local opti- mum by data segmentation is proposed. The segmentation of data is based on the second derivatives computed by a Fourier devel- opment method. A filtering process is used to achieve acceptable segmentation. Numerical results are presented by using the data segmentation method, compared with the regularization method. For further investigation, the proposed algorithm is applied to the resistance capacitance （RC） networks identification problem, and improvements of the result are obtained by using this algorithm.展开更多
A partition checkpoint strategy based on data segment priority is presented to meet the timing constraints of the data and the transaction in embedded real-time main memory database systems（ERTMMDBS） as well as to r...A partition checkpoint strategy based on data segment priority is presented to meet the timing constraints of the data and the transaction in embedded real-time main memory database systems（ERTMMDBS） as well as to reduce the number of the transactions missing their deadlines and the recovery time.The partition checkpoint strategy takes into account the characteristics of the data and the transactions associated with it;moreover,it partitions the database according to the data segment priority and sets the corresponding checkpoint frequency to each partition for independent checkpoint operation.The simulation results show that the partition checkpoint strategy decreases the ratio of trans-actions missing their deadlines.展开更多
Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has...Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.展开更多
By systemic processing, comprehensive analysis, and interpretation of gravity data, we confirmed the existence of the west segment of the coastal fault zone(west of Yangjiang to Beibu Bay) in the coastal region of Sou...By systemic processing, comprehensive analysis, and interpretation of gravity data, we confirmed the existence of the west segment of the coastal fault zone(west of Yangjiang to Beibu Bay) in the coastal region of South China. This showed an apparent high gravity gradient in the NEE direction, and worse linearity and less compactness than that in the Pearl River month. This also revealed a relatively large curvature and a complicated gravity structure. In the finding images processed by the gravity data system, each fault was well reflected and primarily characterized by isolines or thick black stripes with a cutting depth greater than 30 km. Though mutually cut by NW-trending and NE-trending faults, the apparent NEE stripe-shaped structure of the west segment of the coastal fault zone remained unchanged,with good continuity and an activity strength higher than that of NW and NE-trending faults. Moreover,we determined that the west segment of the coastal fault zone is the major seismogenic structure responsible for strong earthquakes in the coastal region in the border area of Guangdong, Guangxi, and Hainan.展开更多
Enterprises have vast amounts of customer behavior data in the era of big data. How to take advantage of these data to evaluate custom forfeit risks effectively is a common issue faced by enterprises. Most of traditio...Enterprises have vast amounts of customer behavior data in the era of big data. How to take advantage of these data to evaluate custom forfeit risks effectively is a common issue faced by enterprises. Most of traditional customer churn predicting models ignore customer segmentation and misclassification cost, which reduces the rationality of model. Dealing with these deficiencies, we established a research model of customer churn based on customer segmentation and misclassification cost. We utilized this model to analyze customer behavior data of a telecom company. The results show that this model is better than those models without customer segmentation and misclassification cost in terms of the performance, accuracy and coverage of model.展开更多
The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is th...The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.展开更多
In this study, Landsat 5 Thematic Mapper (TM) and SPOT HRV Panchromatic data were analysed to determine the geometry of an active fault segment (the Ganos segment) in Gazikoy-Saros region, west of Marmara Sea, Turkey....In this study, Landsat 5 Thematic Mapper (TM) and SPOT HRV Panchromatic data were analysed to determine the geometry of an active fault segment (the Ganos segment) in Gazikoy-Saros region, west of Marmara Sea, Turkey. Gazikoy-Saros/Ganos segment is a part of North Anatolian Fault Zone (NAFZ). North-Anatolian fault is considered to be one of the most important active strike-slip faults in the world. Thus far in relevant researches based on Gazikoy-Saros segment a single straight fault line representation is used on the fault descriptive geological maps. This study, with the aid of enhanced remotely sensed data aims to reveal the linear details of the NAFZ fault segment, which subsequently were superposed with a Digital Elevation Model (DEM) data. Respectively, using these data the surface geometry expression of Gazikoy-Saros fault segment was detailed and remapped. According to the results of the analysis two small releasing steps were identified on this segment. The first one is situated between Mürseli and Güzelkoy villages, and the second one is between Mürseli and Yorguc villages. In addition to this, it is found that the fault strike bends approximately 7° further to in south-eastern (SE) direction between Yenikoy and Sofular villages. This angular change was defined with the advantage of multi-angular viewing capability of the multi-satellite sensors and DEM data. The newly generated surface geometry expression of Ganos segment was compared with Global Positioning System (GPS) velocity vectors.展开更多
文摘In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.
基金supported by the National Basic Research Program of China(2011CB013103)
文摘A new numerical differentiation method with local opti- mum by data segmentation is proposed. The segmentation of data is based on the second derivatives computed by a Fourier devel- opment method. A filtering process is used to achieve acceptable segmentation. Numerical results are presented by using the data segmentation method, compared with the regularization method. For further investigation, the proposed algorithm is applied to the resistance capacitance （RC） networks identification problem, and improvements of the result are obtained by using this algorithm.
基金Supported by the National Natural Science Foundation of China (60673128)
文摘A partition checkpoint strategy based on data segment priority is presented to meet the timing constraints of the data and the transaction in embedded real-time main memory database systems（ERTMMDBS） as well as to reduce the number of the transactions missing their deadlines and the recovery time.The partition checkpoint strategy takes into account the characteristics of the data and the transactions associated with it;moreover,it partitions the database according to the data segment priority and sets the corresponding checkpoint frequency to each partition for independent checkpoint operation.The simulation results show that the partition checkpoint strategy decreases the ratio of trans-actions missing their deadlines.
文摘Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.
基金financially supported by Guangdong Provincial Science and Technology Plan Projects(20178030314082)General Project of National Natural Science Foundation of China (41676057)National Science and Technology Support Program (2015BAK18B01)
文摘By systemic processing, comprehensive analysis, and interpretation of gravity data, we confirmed the existence of the west segment of the coastal fault zone(west of Yangjiang to Beibu Bay) in the coastal region of South China. This showed an apparent high gravity gradient in the NEE direction, and worse linearity and less compactness than that in the Pearl River month. This also revealed a relatively large curvature and a complicated gravity structure. In the finding images processed by the gravity data system, each fault was well reflected and primarily characterized by isolines or thick black stripes with a cutting depth greater than 30 km. Though mutually cut by NW-trending and NE-trending faults, the apparent NEE stripe-shaped structure of the west segment of the coastal fault zone remained unchanged,with good continuity and an activity strength higher than that of NW and NE-trending faults. Moreover,we determined that the west segment of the coastal fault zone is the major seismogenic structure responsible for strong earthquakes in the coastal region in the border area of Guangdong, Guangxi, and Hainan.
文摘Enterprises have vast amounts of customer behavior data in the era of big data. How to take advantage of these data to evaluate custom forfeit risks effectively is a common issue faced by enterprises. Most of traditional customer churn predicting models ignore customer segmentation and misclassification cost, which reduces the rationality of model. Dealing with these deficiencies, we established a research model of customer churn based on customer segmentation and misclassification cost. We utilized this model to analyze customer behavior data of a telecom company. The results show that this model is better than those models without customer segmentation and misclassification cost in terms of the performance, accuracy and coverage of model.
文摘The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.
文摘In this study, Landsat 5 Thematic Mapper (TM) and SPOT HRV Panchromatic data were analysed to determine the geometry of an active fault segment (the Ganos segment) in Gazikoy-Saros region, west of Marmara Sea, Turkey. Gazikoy-Saros/Ganos segment is a part of North Anatolian Fault Zone (NAFZ). North-Anatolian fault is considered to be one of the most important active strike-slip faults in the world. Thus far in relevant researches based on Gazikoy-Saros segment a single straight fault line representation is used on the fault descriptive geological maps. This study, with the aid of enhanced remotely sensed data aims to reveal the linear details of the NAFZ fault segment, which subsequently were superposed with a Digital Elevation Model (DEM) data. Respectively, using these data the surface geometry expression of Gazikoy-Saros fault segment was detailed and remapped. According to the results of the analysis two small releasing steps were identified on this segment. The first one is situated between Mürseli and Güzelkoy villages, and the second one is between Mürseli and Yorguc villages. In addition to this, it is found that the fault strike bends approximately 7° further to in south-eastern (SE) direction between Yenikoy and Sofular villages. This angular change was defined with the advantage of multi-angular viewing capability of the multi-satellite sensors and DEM data. The newly generated surface geometry expression of Ganos segment was compared with Global Positioning System (GPS) velocity vectors.
