With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter...With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.展开更多
The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flo...The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.展开更多
This work concerns the field of diagnostic aids that facilitate diagnostic decisions for practitioners, especially in medical imaging. The pathology in question, in this study, is the renal cyst. The diagnostic proces...This work concerns the field of diagnostic aids that facilitate diagnostic decisions for practitioners, especially in medical imaging. The pathology in question, in this study, is the renal cyst. The diagnostic process starts from simultaneous acquisitions of double isotope (Teechnetium-99 m and Iodine-131) scintigraphic images. Then, the platform allows the fusion of these images and the calculation of a pathological parameter that permits the characterization of the state of the dysplasic kidney by comparing it with the normal one. The final result is fusion images annotated by the pathological parameter value.展开更多
In the context of this paper, a small scale, medium precision, stabilized pan/tilt platform is developed as a prototype, which is used to compare various stabilization algorithms experimentally. The overall performanc...In the context of this paper, a small scale, medium precision, stabilized pan/tilt platform is developed as a prototype, which is used to compare various stabilization algorithms experimentally. The overall performance of the system depends on rigid body dynamics, structural dynamics, servo control loops, stabilization control algorithm, sensor fusion algorithm and sensory feedback such as from the IMU （inertial measurement unit）. In the case that the response bandwidth of the overall system is high enough, the same hardware can also achieve active vibration isolation. All of these design aspects are investigated in the paper via numerical models and with their experimental verification.展开更多
SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an ...SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an earthquake forecasting model. The main function of this system is to analyze and process the deformation, fluid, electromagnetic and other geophysical field observing data from ground-based observation, as well as space-based observation. Combined station and earthquake distributions, geological structure and other information, this system can provide a basic software platform for earthquake forecasting research based on spatiotemporal fusion. The hierarchical station tree for data sifting and the interaction mode have been innovatively developed in this SeisGuard system to improve users’ working efficiency. The data storage framework designed according to the characteristics of different time series can unify the interfaces of different data sources, provide the support of data flow, simplify the management and usage of data, and provide foundation for analysis of big data. The final aim of this development is to establish an effective earthquake forecasting model combined all available information from ground-based observations to space-based observations.展开更多
Facing the development of"Internet+"and the demand of diversified talents in the market,excellent talents with computer operation ability are increasingly needed by all trades and professions.This"abili...Facing the development of"Internet+"and the demand of diversified talents in the market,excellent talents with computer operation ability are increasingly needed by all trades and professions.This"ability"added to professional skills will enhance the employment competitiveness to a great extent.Therefore,the traditional employment mode and talent training mode need to be transformed by various integrated means to adapt to the change in employment status.By analyzing the current situation and employment of agricultural application-oriented universities,the fusion course is put forward in this paper,taking the construction of the Office Automation course,a platform course of Heilongjiang Bayi Agricultural University,as an example.展开更多
随着社交网络平台的迅速发展,网络欺凌问题日益突出,文本与图片相结合的多样化网络表达形式提高了网络欺凌的检测和治理难度.构建了一个包含文本和图片的中文多模态网络欺凌数据集,将BERT(bidirectional encoder representations from t...随着社交网络平台的迅速发展,网络欺凌问题日益突出,文本与图片相结合的多样化网络表达形式提高了网络欺凌的检测和治理难度.构建了一个包含文本和图片的中文多模态网络欺凌数据集,将BERT(bidirectional encoder representations from transformers)模型与ResNet50模型相结合,分别提取文本和图片的单模态特征,并进行决策层融合,对融合后的特征进行检测,实现了对网络欺凌与非网络欺凌2个类别的文本和图片的准确识别.实验结果表明,提出的多模态网络欺凌检测模型能够有效识别出包含文本与图片的具有网络欺凌性质的社交网络帖子或者评论,提高了多模态形式网络欺凌检测的实用性、准确性和效率,为社交网络平台的网络欺凌检测和治理提供了一种新的思路和方法,有助于构建更加健康、文明的网络环境.展开更多
文摘With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.
基金support provided by the National Natural Sciences Foundation of China(No.41771419)Student Research Training Program of Southwest Jiaotong University(No.191510,No.182117)。
文摘The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.
文摘This work concerns the field of diagnostic aids that facilitate diagnostic decisions for practitioners, especially in medical imaging. The pathology in question, in this study, is the renal cyst. The diagnostic process starts from simultaneous acquisitions of double isotope (Teechnetium-99 m and Iodine-131) scintigraphic images. Then, the platform allows the fusion of these images and the calculation of a pathological parameter that permits the characterization of the state of the dysplasic kidney by comparing it with the normal one. The final result is fusion images annotated by the pathological parameter value.
文摘In the context of this paper, a small scale, medium precision, stabilized pan/tilt platform is developed as a prototype, which is used to compare various stabilization algorithms experimentally. The overall performance of the system depends on rigid body dynamics, structural dynamics, servo control loops, stabilization control algorithm, sensor fusion algorithm and sensory feedback such as from the IMU （inertial measurement unit）. In the case that the response bandwidth of the overall system is high enough, the same hardware can also achieve active vibration isolation. All of these design aspects are investigated in the paper via numerical models and with their experimental verification.
文摘SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an earthquake forecasting model. The main function of this system is to analyze and process the deformation, fluid, electromagnetic and other geophysical field observing data from ground-based observation, as well as space-based observation. Combined station and earthquake distributions, geological structure and other information, this system can provide a basic software platform for earthquake forecasting research based on spatiotemporal fusion. The hierarchical station tree for data sifting and the interaction mode have been innovatively developed in this SeisGuard system to improve users’ working efficiency. The data storage framework designed according to the characteristics of different time series can unify the interfaces of different data sources, provide the support of data flow, simplify the management and usage of data, and provide foundation for analysis of big data. The final aim of this development is to establish an effective earthquake forecasting model combined all available information from ground-based observations to space-based observations.
基金The 2021 Heilongjiang Education Scientific Planning Key Project"Research and Practice of‘Multi-Course Fusion’Online and Ofline Hybrid Teaching Mode Based on Improving Employment Competitiveness-Taking Computer Basic Course as An Example"(Project Number:GJB1421199)The 2019 Teaching Research Project of Heilongjiang Bayi A gricultural University"Online and Offline Hybrid Teaching of Office Automation Course"(Project Number:NDJY1918)+1 种基金The 2021 Teaching Research Project of Heilongjiang Bayi Agricultural University"Machine Learming Course Based on Case Teaching in Application Exploration of Big Data Specialty"(Project Number:NDJY2121)The Daqing Guiding Science and Technology Plan Project"Research and Construction of Multi Factor Environmental Prediction Algorithm for Pigsty Based on Attention Mechanism"(Project Number:Zd-2021-68)。
文摘Facing the development of"Internet+"and the demand of diversified talents in the market,excellent talents with computer operation ability are increasingly needed by all trades and professions.This"ability"added to professional skills will enhance the employment competitiveness to a great extent.Therefore,the traditional employment mode and talent training mode need to be transformed by various integrated means to adapt to the change in employment status.By analyzing the current situation and employment of agricultural application-oriented universities,the fusion course is put forward in this paper,taking the construction of the Office Automation course,a platform course of Heilongjiang Bayi Agricultural University,as an example.
文摘随着社交网络平台的迅速发展,网络欺凌问题日益突出,文本与图片相结合的多样化网络表达形式提高了网络欺凌的检测和治理难度.构建了一个包含文本和图片的中文多模态网络欺凌数据集,将BERT(bidirectional encoder representations from transformers)模型与ResNet50模型相结合,分别提取文本和图片的单模态特征,并进行决策层融合,对融合后的特征进行检测,实现了对网络欺凌与非网络欺凌2个类别的文本和图片的准确识别.实验结果表明,提出的多模态网络欺凌检测模型能够有效识别出包含文本与图片的具有网络欺凌性质的社交网络帖子或者评论,提高了多模态形式网络欺凌检测的实用性、准确性和效率,为社交网络平台的网络欺凌检测和治理提供了一种新的思路和方法,有助于构建更加健康、文明的网络环境.
