Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-...Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin,the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD).Because the analysis is costly and dependent on experts,a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data.However,the model’s performance was limited because of improper preprocessing of the intensity profile.Because preprocessing was applied to each feature,the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition.In this study,the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis.For 49 test data among the 488 data,the convolutional neural network(CNN)model improved the average absolute error and coefficient of determination by 41%and 46%,respectively,than those of CNN model with feature-based pre-processing.This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data.The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau.In addition,the overall procedure can be applied to any XRD data of sediments worldwide.展开更多
This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model...This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model follows a“three-stage”and“two-subject”framework,incorporating a structured design for teaching content and assessment methods before,during,and after class.Practical results indicate that this approach significantly enhances teaching effectiveness and improves students’learning autonomy.展开更多
In-optical-sensor computing architectures based on neuro-inspired optical sensor arrays have become key milestones for in-sensor artificial intelligence(AI)technology,enabling intelligent vision sensing and extensive ...In-optical-sensor computing architectures based on neuro-inspired optical sensor arrays have become key milestones for in-sensor artificial intelligence(AI)technology,enabling intelligent vision sensing and extensive data processing.These architectures must demonstrate potential advantages in terms of mass production and complementary metal oxide semiconductor compatibility.Here,we introduce a visible-light-driven neuromorphic vision system that integrates front-end retinomorphic photosensors with a back-end artificial neural network(ANN),employing a single neuro-inspired indium-g allium-zinc-oxide photo transistor(NIP)featuring an aluminum sensitization layer(ASL).By methodically adjusting the ASL coverage on IGZO phototransistors,a fast-switching response-type and a synaptic response-type of IGZO photo transistors are successfully developed.Notably,the fabricated NIP shows a remarkable retina-like photoinduced synaptic plasticity under wavelengths up to 635 nm,with over256-states,weight update nonlinearity below 0.1,and a dynamic range of 64.01.Owing to this technology,a 6×6 neuro-inspired optical image sensor array with the NIP can perform highly integrated sensing,memory,and preprocessing functions,including contrast enhancement,and handwritten digit image recognition.The demonstrated prototype highlights the potential for efficient hardware implementations in in-sensor AI technologies.展开更多
The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For ...The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For this,a TBM big data preprocessing method in ML was proposed in the present study.It emphasized the accurate division of TBM tunneling cycle and the optimization method of feature extraction.Based on the data collected from a TBM water conveyance tunnel in China,its effectiveness was demonstrated by application in predicting TBM performance.Firstly,the Score-Kneedle(S-K)method was proposed to divide a TBM tunneling cycle into five phases.Conducted on 500 TBM tunneling cycles,the S-K method accurately divided all five phases in 458 cycles(accuracy of 91.6%),which is superior to the conventional duration division method(accuracy of 74.2%).Additionally,the S-K method accurately divided the stable phase in 493 cycles(accuracy of 98.6%),which is superior to two state-of-the-art division methods,namely the histogram discriminant method(accuracy of 94.6%)and the cumulative sum change point detection method(accuracy of 92.8%).Secondly,features were extracted from the divided phases.Specifically,TBM tunneling resistances were extracted from the free rotating phase and free advancing phase.The resistances were subtracted from the total forces to represent the true rock-fragmentation forces.The secant slope and the mean value were extracted as features of the increasing phase and stable phase,respectively.Finally,an ML model integrating a deep neural network and genetic algorithm(GA-DNN)was established to learn the preprocessed data.The GA-DNN used 6 secant slope features extracted from the increasing phase to predict the mean field penetration index(FPI)and torque penetration index(TPI)in the stable phase,guiding TBM drivers to make better decisions in advance.The results indicate that the proposed TBM big data preprocessing method can improve prediction accuracy significantly(improving R2s of TPI and FPI on the test dataset from 0.7716 to 0.9178 and from 0.7479 to 0.8842,respectively).展开更多
As one of the main methods of microbial community functional diversity measurement, biolog method was favored by many researchers for its simple oper- ation, high sensitivity, strong resolution and rich data. But the ...As one of the main methods of microbial community functional diversity measurement, biolog method was favored by many researchers for its simple oper- ation, high sensitivity, strong resolution and rich data. But the preprocessing meth- ods reported in the literatures were not the same. In order to screen the best pre- processing method, this paper took three typical treatments to explore the effect of different preprocessing methods on soil microbial community functional diversity. The results showed that, method B＇s overall trend of AWCD values was better than A and C＇s. Method B＇s microbial utilization of six carbon sources was higher, and the result was relatively stable. The Simpson index, Shannon richness index and Car- bon source utilization richness index of the two treatments were B〉C〉A, while the Mclntosh index and Shannon evenness were not very stable, but the difference of variance analysis was not significant, and the method B was always with a smallest variance. Method B＇s principal component analysis was better than A and C＇s. In a word, the method using 250 r/min shaking for 30 minutes and cultivating at 28 ℃ was the best one, because it was simple, convenient, and with good repeatability.展开更多
This paper discusses some aspects of finite element computation,such as the automatic generation of finite element ,refinement of mesh,process of node density, distribution of load,optimum design and the drawing o...This paper discusses some aspects of finite element computation,such as the automatic generation of finite element ,refinement of mesh,process of node density, distribution of load,optimum design and the drawing of stress contour, and describes the developing process of software for a planar 8 node element.展开更多
The recent proliferation of the 3D reflection seismic method into the near-surface area of geophysical applications, especially in response to the emergence of the need to comprehensively characterize and monitor near...The recent proliferation of the 3D reflection seismic method into the near-surface area of geophysical applications, especially in response to the emergence of the need to comprehensively characterize and monitor near-surface carbon dioxide sequestration in shallow saline aquifers around the world, justifies the emphasis on cost-effective and robust quality control and assurance （QC/QA） workflow of 3D seismic data preprocessing that is suitable for near-surface applications. The main purpose of our seismic data preprocessing QC is to enable the use of appropriate header information, data that are free of noise-dominated traces, and/or flawed vertical stacking in subsequent processing steps. In this article, I provide an account of utilizing survey design specifications, noise properties, first breaks, and normal moveout for rapid and thorough graphical QC/QA diagnostics, which are easy to apply and efficient in the diagnosis of inconsistencies. A correlated vibroseis time-lapse 3D-seismic data set from a CO2-flood monitoring survey is used for demonstrating QC diagnostics. An important by-product of the QC workflow is establishing the number of layers for a refraction statics model in a data-driven graphical manner that capitalizes on the spatial coverage of the 3D seismic data.展开更多
The Moon-based Ultraviolet Telescope （MUVT） is one of the payloads on the Chang＇e-3 （CE-3） lunar lander. Because of the advantages of having no at- mospheric disturbances and the slow rotation of the Moon, we can...The Moon-based Ultraviolet Telescope （MUVT） is one of the payloads on the Chang＇e-3 （CE-3） lunar lander. Because of the advantages of having no at- mospheric disturbances and the slow rotation of the Moon, we can make long-term continuous observations of a series of important celestial objects in the near ultra- violet band （245-340 nm）, and perform a sky survey of selected areas, which can- not be completed on Earth. We can find characteristic changes in celestial brightness with time by analyzing image data from the MUVT, and deduce the radiation mech- anism and physical properties of these celestial objects after comparing with a phys- ical model. In order to explain the scientific purposes of MUVT, this article analyzes the preprocessing of MUVT image data and makes a preliminary evaluation of data quality. The results demonstrate that the methods used for data collection and prepro- cessing are effective, and the Level 2A and 2B image data satisfy the requirements of follow-up scientific researches.展开更多
The conventional poststack inversion uses standard recursion formulas to obtain impedance in a single trace.It cannot allow for lateral regularization.In this paper,ID edge-preserving smoothing(EPS)fi lter is extended...The conventional poststack inversion uses standard recursion formulas to obtain impedance in a single trace.It cannot allow for lateral regularization.In this paper,ID edge-preserving smoothing(EPS)fi lter is extended to 2D/3D for setting precondition of impedance model in impedance inversion.The EPS filter incorporates a priori knowledge into the seismic inversion.The a priori knowledge incorporated from EPS filter preconditioning relates to the blocky features of the impedance model,which makes the formation interfaces and geological edges precise and keeps the inversion procedure robust.Then,the proposed method is performed on two 2D models to show its feasibility and stability.Last,the proposed method is performed on a real 3D seismic work area from Southwest China to predict reef reservoirs in practice.展开更多
Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid appr...Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier(VQC),which development seems promising.Albeit being largely studied,VQC implementations for“real-world”datasets are still challenging on Noisy Intermediate Scale Quantum devices(NISQ).In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping.This pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ devices.By including feature selection techniques and geometrical transformations,enhanced quantum state preparation is achieved.Also,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the data.Our results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients.We used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.展开更多
In order to achieve high quality images with time-delayed integration(TDI) charge-coupled device(CCD) imaging system, an improved adaptive preprocessing method is proposed with functions of both denoising and edge enh...In order to achieve high quality images with time-delayed integration(TDI) charge-coupled device(CCD) imaging system, an improved adaptive preprocessing method is proposed with functions of both denoising and edge enhancement. It is a weighted average filter integrating the average filter and the improved range filter. The weighted factors are deduced in terms of a cost function, which are adjustable to different images. To validate the proposed method, extensive tests are carried out on a developed TDI CCD imaging system. The experimental results confirm that this preprocessing method can fulfill the noise removal and edge sharpening simultaneously, which can play an important role in remote sensing field.展开更多
Due to the frequent changes of wind speed and wind direction,the accuracy of wind turbine(WT)power prediction using traditional data preprocessing method is low.This paper proposes a data preprocessing method which co...Due to the frequent changes of wind speed and wind direction,the accuracy of wind turbine(WT)power prediction using traditional data preprocessing method is low.This paper proposes a data preprocessing method which combines POT with DBSCAN(POT-DBSCAN)to improve the prediction efficiency of wind power prediction model.Firstly,according to the data of WT in the normal operation condition,the power prediction model ofWT is established based on the Particle Swarm Optimization(PSO)Arithmetic which is combined with the BP Neural Network(PSO-BP).Secondly,the wind-power data obtained from the supervisory control and data acquisition(SCADA)system is preprocessed by the POT-DBSCAN method.Then,the power prediction of the preprocessed data is carried out by PSO-BP model.Finally,the necessity of preprocessing is verified by the indexes.This case analysis shows that the prediction result of POT-DBSCAN preprocessing is better than that of the Quartile method.Therefore,the accuracy of data and prediction model can be improved by using this method.展开更多
It is one of the major challenges for face recognition to minimize the disadvantage of il- lumination variations of face images in different scenarios. Local Binary Pattern (LBP) has been proved to be successful for f...It is one of the major challenges for face recognition to minimize the disadvantage of il- lumination variations of face images in different scenarios. Local Binary Pattern (LBP) has been proved to be successful for face recognition. However, it is still very rare to take LBP as an illumination preprocessing approach. In this paper, we propose a new LBP-based multi-scale illumination pre- processing method. This method mainly includes three aspects: threshold adjustment, multi-scale addition and symmetry restoration/neighborhood replacement. Our experiment results show that the proposed method performs better than the existing LBP-based methods at the point of illumination preprocessing. Moreover, compared with some face image preprocessing methods, such as histogram equalization, Gamma transformation, Retinex, and simplified LBP operator, our method can effectively improve the robustness for face recognition against illumination variation, and achieve higher recog- nition rate.展开更多
New adaptive preprocessing algorithms based on the polar coordinate system were put forward to get high-precision corneal topography calculation results. Adaptive locating algorithms of concentric circle center were c...New adaptive preprocessing algorithms based on the polar coordinate system were put forward to get high-precision corneal topography calculation results. Adaptive locating algorithms of concentric circle center were created to accurately capture the circle center of original Placido-based image, expand the image into matrix centered around the circle center, and convert the matrix into the polar coordinate system with the circle center as pole. Adaptive image smoothing treatment was followed and the characteristics of useful circles were extracted via horizontal edge detection, based on useful circles presenting approximate horizontal lines while noise signals presenting vertical lines or different angles. Effective combination of different operators of morphology were designed to remedy data loss caused by noise disturbances, get complete image about circle edge detection to satisfy the requests of precise calculation on follow-up parameters. The experimental data show that the algorithms meet the requirements of practical detection with characteristics of less data loss, higher data accuracy and easier availability.展开更多
Text classification is an essential task of natural language processing. Preprocessing, which determines the representation of text features, is one of the key steps of text classification architecture. It proposed a ...Text classification is an essential task of natural language processing. Preprocessing, which determines the representation of text features, is one of the key steps of text classification architecture. It proposed a novel efficient and effective preprocessing algorithm with three methods for text classification combining the Orthogonal Matching Pursuit algorithm to perform the classification. The main idea of the novel preprocessing strategy is that it combined stopword removal and/or regular filtering with tokenization and lowercase conversion, which can effectively reduce the feature dimension and improve the text feature matrix quality. Simulation tests on the 20 newsgroups dataset show that compared with the existing state-of-the-art method, the new method reduces the number of features by 19.85%, 34.35%, 26.25% and 38.67%, improves accuracy by 7.36%, 8.8%, 5.71% and 7.73%, and increases the speed of text classification by 17.38%, 25.64%, 23.76% and 33.38% on the four data, respectively.展开更多
At low bitrate, all block discrete cosine transform (BDCT) based video coding algorithms suffer from visible blocking and ringing artifacts in the reconstructed images because the quantization is too coarse and high f...At low bitrate, all block discrete cosine transform (BDCT) based video coding algorithms suffer from visible blocking and ringing artifacts in the reconstructed images because the quantization is too coarse and high frequency DCT coefficients are inclined to be quantized to zeros. Preprocessing algorithms can enhance coding efficiency and thus reduce the likelihood of blocking artifacts and ringing artifacts generated in the video coding process by applying a low-pass filter before video encoding to remove some relatively insignificant high frequent components. In this paper, we introduce a new adaptive preprocessing algo- rithm, which employs an improved bilateral filter to provide adaptive edge-preserving low-pass filtering which is adjusted ac- cording to the quantization parameters. Whether at low or high bit rate, the preprocessing can provide proper filtering to make the video encoder more efficient and have better reconstructed image quality. Experimental results demonstrate that our proposed preprocessing algorithm can significantly improve both subjective and objective quality.展开更多
Many classifiers and methods are proposed to deal with letter recognition problem. Among them, clustering is a widely used method. But only one time for clustering is not adequately. Here, we adopt data preprocessing ...Many classifiers and methods are proposed to deal with letter recognition problem. Among them, clustering is a widely used method. But only one time for clustering is not adequately. Here, we adopt data preprocessing and a re kernel clustering method to tackle the letter recognition problem. In order to validate effectiveness and efficiency of proposed method, we introduce re kernel clustering into Kernel Nearest Neighbor classification(KNN), Radial Basis Function Neural Network(RBFNN), and Support Vector Machine(SVM). Furthermore, we compare the difference between re kernel clustering and one time kernel clustering which is denoted as kernel clustering for short. Experimental results validate that re kernel clustering forms fewer and more feasible kernels and attain higher classification accuracy.展开更多
基金supported by the Gas Hydrate R&D Organization and the Korea Institute of Geoscience and Mineral Resources(KIGAM)(GP2021-010)supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2021R1C1C1004460)Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korean government(MOTIE)(20214000000500,Training Program of CCUS for Green Growth).
文摘Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin,the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD).Because the analysis is costly and dependent on experts,a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data.However,the model’s performance was limited because of improper preprocessing of the intensity profile.Because preprocessing was applied to each feature,the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition.In this study,the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis.For 49 test data among the 488 data,the convolutional neural network(CNN)model improved the average absolute error and coefficient of determination by 41%and 46%,respectively,than those of CNN model with feature-based pre-processing.This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data.The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau.In addition,the overall procedure can be applied to any XRD data of sediments worldwide.
基金2024 Anqing Normal University University-Level Key Project(ZK2024062D)。
文摘This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model follows a“three-stage”and“two-subject”framework,incorporating a structured design for teaching content and assessment methods before,during,and after class.Practical results indicate that this approach significantly enhances teaching effectiveness and improves students’learning autonomy.
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(Grant No.RS-2023-00256917)Samsung Display。
文摘In-optical-sensor computing architectures based on neuro-inspired optical sensor arrays have become key milestones for in-sensor artificial intelligence(AI)technology,enabling intelligent vision sensing and extensive data processing.These architectures must demonstrate potential advantages in terms of mass production and complementary metal oxide semiconductor compatibility.Here,we introduce a visible-light-driven neuromorphic vision system that integrates front-end retinomorphic photosensors with a back-end artificial neural network(ANN),employing a single neuro-inspired indium-g allium-zinc-oxide photo transistor(NIP)featuring an aluminum sensitization layer(ASL).By methodically adjusting the ASL coverage on IGZO phototransistors,a fast-switching response-type and a synaptic response-type of IGZO photo transistors are successfully developed.Notably,the fabricated NIP shows a remarkable retina-like photoinduced synaptic plasticity under wavelengths up to 635 nm,with over256-states,weight update nonlinearity below 0.1,and a dynamic range of 64.01.Owing to this technology,a 6×6 neuro-inspired optical image sensor array with the NIP can perform highly integrated sensing,memory,and preprocessing functions,including contrast enhancement,and handwritten digit image recognition.The demonstrated prototype highlights the potential for efficient hardware implementations in in-sensor AI technologies.
基金The support provided by the Natural Science Foundation of Hubei Province(Grant No.2021CFA081)the National Natural Science Foundation of China(Grant No.42277160)the fellowship of China Postdoctoral Science Foundation(Grant No.2022TQ0241)is gratefully acknowledged.
文摘The big data generated by tunnel boring machines(TBMs)are widely used to reveal complex rock-machine interactions by machine learning(ML)algorithms.Data preprocessing plays a crucial role in improving ML accuracy.For this,a TBM big data preprocessing method in ML was proposed in the present study.It emphasized the accurate division of TBM tunneling cycle and the optimization method of feature extraction.Based on the data collected from a TBM water conveyance tunnel in China,its effectiveness was demonstrated by application in predicting TBM performance.Firstly,the Score-Kneedle(S-K)method was proposed to divide a TBM tunneling cycle into five phases.Conducted on 500 TBM tunneling cycles,the S-K method accurately divided all five phases in 458 cycles(accuracy of 91.6%),which is superior to the conventional duration division method(accuracy of 74.2%).Additionally,the S-K method accurately divided the stable phase in 493 cycles(accuracy of 98.6%),which is superior to two state-of-the-art division methods,namely the histogram discriminant method(accuracy of 94.6%)and the cumulative sum change point detection method(accuracy of 92.8%).Secondly,features were extracted from the divided phases.Specifically,TBM tunneling resistances were extracted from the free rotating phase and free advancing phase.The resistances were subtracted from the total forces to represent the true rock-fragmentation forces.The secant slope and the mean value were extracted as features of the increasing phase and stable phase,respectively.Finally,an ML model integrating a deep neural network and genetic algorithm(GA-DNN)was established to learn the preprocessed data.The GA-DNN used 6 secant slope features extracted from the increasing phase to predict the mean field penetration index(FPI)and torque penetration index(TPI)in the stable phase,guiding TBM drivers to make better decisions in advance.The results indicate that the proposed TBM big data preprocessing method can improve prediction accuracy significantly(improving R2s of TPI and FPI on the test dataset from 0.7716 to 0.9178 and from 0.7479 to 0.8842,respectively).
基金Supported by National and International Scientific and Technological Cooperation Project"The application of Microbial Agents on Mining Reclamation and Ecological Recovery"(2011DFR31230)Key Project of Shanxi academy of Agricultural Science"The Research and Application of Bio-organic Fertilizer on Mining Reclamation and Soil Remediation"(2013zd12)Major Science and Technology Programs of Shanxi Province"Key Technology Research and Demonstration of mining waste land ecosystem Restoration and Reconstruction"(20121101009)~~
文摘As one of the main methods of microbial community functional diversity measurement, biolog method was favored by many researchers for its simple oper- ation, high sensitivity, strong resolution and rich data. But the preprocessing meth- ods reported in the literatures were not the same. In order to screen the best pre- processing method, this paper took three typical treatments to explore the effect of different preprocessing methods on soil microbial community functional diversity. The results showed that, method B＇s overall trend of AWCD values was better than A and C＇s. Method B＇s microbial utilization of six carbon sources was higher, and the result was relatively stable. The Simpson index, Shannon richness index and Car- bon source utilization richness index of the two treatments were B〉C〉A, while the Mclntosh index and Shannon evenness were not very stable, but the difference of variance analysis was not significant, and the method B was always with a smallest variance. Method B＇s principal component analysis was better than A and C＇s. In a word, the method using 250 r/min shaking for 30 minutes and cultivating at 28 ℃ was the best one, because it was simple, convenient, and with good repeatability.
文摘This paper discusses some aspects of finite element computation,such as the automatic generation of finite element ,refinement of mesh,process of node density, distribution of load,optimum design and the drawing of stress contour, and describes the developing process of software for a planar 8 node element.
基金supported by the U.S. Department of Energy (No. DE-FC26-03NT15414)
文摘The recent proliferation of the 3D reflection seismic method into the near-surface area of geophysical applications, especially in response to the emergence of the need to comprehensively characterize and monitor near-surface carbon dioxide sequestration in shallow saline aquifers around the world, justifies the emphasis on cost-effective and robust quality control and assurance （QC/QA） workflow of 3D seismic data preprocessing that is suitable for near-surface applications. The main purpose of our seismic data preprocessing QC is to enable the use of appropriate header information, data that are free of noise-dominated traces, and/or flawed vertical stacking in subsequent processing steps. In this article, I provide an account of utilizing survey design specifications, noise properties, first breaks, and normal moveout for rapid and thorough graphical QC/QA diagnostics, which are easy to apply and efficient in the diagnosis of inconsistencies. A correlated vibroseis time-lapse 3D-seismic data set from a CO2-flood monitoring survey is used for demonstrating QC diagnostics. An important by-product of the QC workflow is establishing the number of layers for a refraction statics model in a data-driven graphical manner that capitalizes on the spatial coverage of the 3D seismic data.
文摘The Moon-based Ultraviolet Telescope （MUVT） is one of the payloads on the Chang＇e-3 （CE-3） lunar lander. Because of the advantages of having no at- mospheric disturbances and the slow rotation of the Moon, we can make long-term continuous observations of a series of important celestial objects in the near ultra- violet band （245-340 nm）, and perform a sky survey of selected areas, which can- not be completed on Earth. We can find characteristic changes in celestial brightness with time by analyzing image data from the MUVT, and deduce the radiation mech- anism and physical properties of these celestial objects after comparing with a phys- ical model. In order to explain the scientific purposes of MUVT, this article analyzes the preprocessing of MUVT image data and makes a preliminary evaluation of data quality. The results demonstrate that the methods used for data collection and prepro- cessing are effective, and the Level 2A and 2B image data satisfy the requirements of follow-up scientific researches.
基金The National Key S&T Special Projects (No. 2017ZX05008004-008)the National Natural Science Foundation of China (No. 41874146)+2 种基金the National Natural Science Foundation of China (No. 41704134)the Innovation Team of Youth Scientific and Technological in Southwest Petroleum University (No. 2017CXTD08)the Initiative Projects for Ph.Din China West Normal University (No. 19E063)
文摘The conventional poststack inversion uses standard recursion formulas to obtain impedance in a single trace.It cannot allow for lateral regularization.In this paper,ID edge-preserving smoothing(EPS)fi lter is extended to 2D/3D for setting precondition of impedance model in impedance inversion.The EPS filter incorporates a priori knowledge into the seismic inversion.The a priori knowledge incorporated from EPS filter preconditioning relates to the blocky features of the impedance model,which makes the formation interfaces and geological edges precise and keeps the inversion procedure robust.Then,the proposed method is performed on two 2D models to show its feasibility and stability.Last,the proposed method is performed on a real 3D seismic work area from Southwest China to predict reef reservoirs in practice.
基金funded by eVIDA Research group IT-905-16 from Basque Government.
文摘Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier(VQC),which development seems promising.Albeit being largely studied,VQC implementations for“real-world”datasets are still challenging on Noisy Intermediate Scale Quantum devices(NISQ).In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping.This pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ devices.By including feature selection techniques and geometrical transformations,enhanced quantum state preparation is achieved.Also,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the data.Our results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients.We used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.
基金supported by the National Key Research and Development Project of China(No.2016YFB0501202)
文摘In order to achieve high quality images with time-delayed integration(TDI) charge-coupled device(CCD) imaging system, an improved adaptive preprocessing method is proposed with functions of both denoising and edge enhancement. It is a weighted average filter integrating the average filter and the improved range filter. The weighted factors are deduced in terms of a cost function, which are adjustable to different images. To validate the proposed method, extensive tests are carried out on a developed TDI CCD imaging system. The experimental results confirm that this preprocessing method can fulfill the noise removal and edge sharpening simultaneously, which can play an important role in remote sensing field.
基金National Natural Science Foundation of China(Nos.51875199 and 51905165)Hunan Natural Science Fund Project(2019JJ50186)the Ke7y Research and Development Program of Hunan Province(No.2018GK2073).
文摘Due to the frequent changes of wind speed and wind direction,the accuracy of wind turbine(WT)power prediction using traditional data preprocessing method is low.This paper proposes a data preprocessing method which combines POT with DBSCAN(POT-DBSCAN)to improve the prediction efficiency of wind power prediction model.Firstly,according to the data of WT in the normal operation condition,the power prediction model ofWT is established based on the Particle Swarm Optimization(PSO)Arithmetic which is combined with the BP Neural Network(PSO-BP).Secondly,the wind-power data obtained from the supervisory control and data acquisition(SCADA)system is preprocessed by the POT-DBSCAN method.Then,the power prediction of the preprocessed data is carried out by PSO-BP model.Finally,the necessity of preprocessing is verified by the indexes.This case analysis shows that the prediction result of POT-DBSCAN preprocessing is better than that of the Quartile method.Therefore,the accuracy of data and prediction model can be improved by using this method.
文摘It is one of the major challenges for face recognition to minimize the disadvantage of il- lumination variations of face images in different scenarios. Local Binary Pattern (LBP) has been proved to be successful for face recognition. However, it is still very rare to take LBP as an illumination preprocessing approach. In this paper, we propose a new LBP-based multi-scale illumination pre- processing method. This method mainly includes three aspects: threshold adjustment, multi-scale addition and symmetry restoration/neighborhood replacement. Our experiment results show that the proposed method performs better than the existing LBP-based methods at the point of illumination preprocessing. Moreover, compared with some face image preprocessing methods, such as histogram equalization, Gamma transformation, Retinex, and simplified LBP operator, our method can effectively improve the robustness for face recognition against illumination variation, and achieve higher recog- nition rate.
基金Project(20120321028-01)supported by Scientific and Technological Key Project of Shanxi Province,ChinaProject(20113101)supported by Postgraduate Innovative Key Project of Shanxi Province,China
文摘New adaptive preprocessing algorithms based on the polar coordinate system were put forward to get high-precision corneal topography calculation results. Adaptive locating algorithms of concentric circle center were created to accurately capture the circle center of original Placido-based image, expand the image into matrix centered around the circle center, and convert the matrix into the polar coordinate system with the circle center as pole. Adaptive image smoothing treatment was followed and the characteristics of useful circles were extracted via horizontal edge detection, based on useful circles presenting approximate horizontal lines while noise signals presenting vertical lines or different angles. Effective combination of different operators of morphology were designed to remedy data loss caused by noise disturbances, get complete image about circle edge detection to satisfy the requests of precise calculation on follow-up parameters. The experimental data show that the algorithms meet the requirements of practical detection with characteristics of less data loss, higher data accuracy and easier availability.
文摘Text classification is an essential task of natural language processing. Preprocessing, which determines the representation of text features, is one of the key steps of text classification architecture. It proposed a novel efficient and effective preprocessing algorithm with three methods for text classification combining the Orthogonal Matching Pursuit algorithm to perform the classification. The main idea of the novel preprocessing strategy is that it combined stopword removal and/or regular filtering with tokenization and lowercase conversion, which can effectively reduce the feature dimension and improve the text feature matrix quality. Simulation tests on the 20 newsgroups dataset show that compared with the existing state-of-the-art method, the new method reduces the number of features by 19.85%, 34.35%, 26.25% and 38.67%, improves accuracy by 7.36%, 8.8%, 5.71% and 7.73%, and increases the speed of text classification by 17.38%, 25.64%, 23.76% and 33.38% on the four data, respectively.
基金Project (No. 2006CB303104) supported by the National Basic Re-search Program (973) of China
文摘At low bitrate, all block discrete cosine transform (BDCT) based video coding algorithms suffer from visible blocking and ringing artifacts in the reconstructed images because the quantization is too coarse and high frequency DCT coefficients are inclined to be quantized to zeros. Preprocessing algorithms can enhance coding efficiency and thus reduce the likelihood of blocking artifacts and ringing artifacts generated in the video coding process by applying a low-pass filter before video encoding to remove some relatively insignificant high frequent components. In this paper, we introduce a new adaptive preprocessing algo- rithm, which employs an improved bilateral filter to provide adaptive edge-preserving low-pass filtering which is adjusted ac- cording to the quantization parameters. Whether at low or high bit rate, the preprocessing can provide proper filtering to make the video encoder more efficient and have better reconstructed image quality. Experimental results demonstrate that our proposed preprocessing algorithm can significantly improve both subjective and objective quality.
基金Supported by the National Science Foundation(No.IIS-9988642)the Multidisciplinary Research Program
文摘Many classifiers and methods are proposed to deal with letter recognition problem. Among them, clustering is a widely used method. But only one time for clustering is not adequately. Here, we adopt data preprocessing and a re kernel clustering method to tackle the letter recognition problem. In order to validate effectiveness and efficiency of proposed method, we introduce re kernel clustering into Kernel Nearest Neighbor classification(KNN), Radial Basis Function Neural Network(RBFNN), and Support Vector Machine(SVM). Furthermore, we compare the difference between re kernel clustering and one time kernel clustering which is denoted as kernel clustering for short. Experimental results validate that re kernel clustering forms fewer and more feasible kernels and attain higher classification accuracy.
