This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by hig...This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by high- and low-frequency.In the high-frequency part the wavelet multiscale was used for the edge detection,and the low-frequency part conducted on segmentation using the entropy iterative threshold selection method.Through the consideration of the image edge and region,a CT image of the thorax was chosen to test the proposed method for the segmentation of the lungs.Experimental results show that the method is efficient to segment the interesting region of an image compared with conventional methods.展开更多
In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is ...In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models are developed for the optimal control of batch processes where end-point properties are required. The model parameters are selected within the Bayesian evidence framework. Based on the model, an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy. Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.展开更多
The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world tasks.Although deep learning-based methods ha...The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world tasks.Although deep learning-based methods have achieved significant improvements in image super-resolution(SR),current CNNbased techniques mainly contain massive parameters and a high computational complexity,limiting their practical applications.In this paper,we present a fast and lightweight framework,named weighted multi-scale residual network(WMRN),for a better tradeoff between SR performance and computational efficiency.With the modified residual structure,depthwise separable convolutions(DS Convs)are employed to improve convolutional operations’efficiency.Furthermore,several weighted multi-scale residual blocks(WMRBs)are stacked to enhance the multi-scale representation capability.In the reconstruction subnetwork,a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image.Extensive experiments were conducted to evaluate the proposed model,and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.展开更多
Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is ...Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.展开更多
Multi-instance registration is a challenging problem in computer vision and robotics,where multiple instances of an object need to be registered in a standard coordinate system.Pioneers followed a non-extensible one-s...Multi-instance registration is a challenging problem in computer vision and robotics,where multiple instances of an object need to be registered in a standard coordinate system.Pioneers followed a non-extensible one-shot framework,which prioritizes the registration of simple and isolated instances,often struggling to accurately register challenging or occluded instances.To address these challenges,we propose the first iterative framework for multi-instance 3D registration(MI-3DReg)in this work,termed instance-by-instance(IBI).It successively registers instances while systematically reducing outliers,starting from the easiest and progressing to more challenging ones.This enhances the likelihood of effectively registering instances that may have been initially overlooked,allowing for successful registration in subsequent iterations.Under the IBI framework,we further propose a sparse-to-dense correspondence-based multi-instance registration method(IBI-S2DC)to enhance the robustness of MI-3DReg.Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC,e.g.,our mean registration F1 score is 12.02%/12.35%higher than the existing state-of-the-art on the synthetic/real datasets.The source codes are available online at https://github.com/caoxy01/IBI.展开更多
OPEN Process Framework(OPF)是使软件开发过程达到CMM5级标准的软件工程框架。文中讨论了基于OPF的软件过程的主要元素及实施过程,并把该过程应用于某油田数据采集系统的开发,实践证明基于OPF的软件过程可以提高团队的开发能力、降低...OPEN Process Framework(OPF)是使软件开发过程达到CMM5级标准的软件工程框架。文中讨论了基于OPF的软件过程的主要元素及实施过程,并把该过程应用于某油田数据采集系统的开发,实践证明基于OPF的软件过程可以提高团队的开发能力、降低风险、有效控制资源,为项目的开发提供了高度清晰的过程框架,规范管理和开发流程。展开更多
为解决国防部体系架构框架(department of defense architecture framework,DoDAF)的视图模型存在动态特性表达困难、决策分析支持缺乏等问题,提出一套体系架构集成迭代设计方法。DoDAF视图模型从多视角整合和表达作战相关信息;ExtendSi...为解决国防部体系架构框架(department of defense architecture framework,DoDAF)的视图模型存在动态特性表达困难、决策分析支持缺乏等问题,提出一套体系架构集成迭代设计方法。DoDAF视图模型从多视角整合和表达作战相关信息;ExtendSim可执行模型模拟作战体系架构在多场景下的涌现行为和动态特性;决策模型通过多目标决策规则对架构方案进行量化分析和选择,最终形成“视图-仿真-决策-迭代”的体系架构集成迭代设计方法,并明确架构模型设计流程与模型间数据交互路径。仿真实验验证了其可行性。展开更多
Covalent organic frameworks(COFs),which are constructed by linking organic building blocks via dynamic covalent bonds,are newly emerged and burgeoning crystalline porous copolymers with features including programmable...Covalent organic frameworks(COFs),which are constructed by linking organic building blocks via dynamic covalent bonds,are newly emerged and burgeoning crystalline porous copolymers with features including programmable topological architecture,pre-designable periodic skeleton,well-defined micro-/meso-pore,large specific surface area,and customizable electroactive functionality.Those benefits make COFs as promising candidates for advanced electrochemical energy storage.Especially,for now,structure engineering of COFs from multiscale aspects has been conducted to enable optimal overall electrochemical performance in terms of structure durability,electrical conductivity,redox activity,and charge storage.In this review,we give a fundamental and insightful study on the correlations between multi-scale structure engineering and eventual electrochemical properties of COFs,started with introducing their basic chemistries and charge storage principles.The careful discussion on the significant achievements in structure engineering of COFs from linkages,redox sites,polygon skeleton,crystal nanostructures,and composite microstructures,and further their effects on the electrochemical behavior of COFs are presented.Finally,the timely cutting-edge perspectives and in-depth insights into COFbased electrodematerials to rationally screen their electrochemical behaviors for addressing future challenges and implementing electrochemical energy storage applications are proposed.展开更多
基金Science Research Foundation of Yunnan Fundamental Research Foundation of Applicationgrant number:2009ZC049M+1 种基金Science Research Foundation for the Overseas Chinese Scholars,State Education Ministrygrant number:2010-1561
文摘This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by high- and low-frequency.In the high-frequency part the wavelet multiscale was used for the edge detection,and the low-frequency part conducted on segmentation using the entropy iterative threshold selection method.Through the consideration of the image edge and region,a CT image of the thorax was chosen to test the proposed method for the segmentation of the lungs.Experimental results show that the method is efficient to segment the interesting region of an image compared with conventional methods.
基金Project supported by the National Natural Science Foundation of China(Grant No.60504033)
文摘In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models are developed for the optimal control of batch processes where end-point properties are required. The model parameters are selected within the Bayesian evidence framework. Based on the model, an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy. Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.
基金the National Natural Science Foundation of China(61772149,61866009,61762028,U1701267,61702169)Guangxi Science and Technology Project(2019GXNSFFA245014,ZY20198016,AD18281079,AD18216004)+1 种基金the Natural Science Foundation of Hunan Province(2020JJ3014)Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics(GIIP202001).
文摘The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world tasks.Although deep learning-based methods have achieved significant improvements in image super-resolution(SR),current CNNbased techniques mainly contain massive parameters and a high computational complexity,limiting their practical applications.In this paper,we present a fast and lightweight framework,named weighted multi-scale residual network(WMRN),for a better tradeoff between SR performance and computational efficiency.With the modified residual structure,depthwise separable convolutions(DS Convs)are employed to improve convolutional operations’efficiency.Furthermore,several weighted multi-scale residual blocks(WMRBs)are stacked to enhance the multi-scale representation capability.In the reconstruction subnetwork,a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image.Extensive experiments were conducted to evaluate the proposed model,and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.
基金supported by the International Research Center of Big Data for Sustainable Development Goals under Grant No.CBAS2022GSP05the Open Fund of State Key Laboratory of Remote Sensing Science under Grant No.6142A01210404the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant No.KLIGIP-2022-B03.
文摘Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.
基金supported in part by the National Natural Science Foundation of China(62372377).
文摘Multi-instance registration is a challenging problem in computer vision and robotics,where multiple instances of an object need to be registered in a standard coordinate system.Pioneers followed a non-extensible one-shot framework,which prioritizes the registration of simple and isolated instances,often struggling to accurately register challenging or occluded instances.To address these challenges,we propose the first iterative framework for multi-instance 3D registration(MI-3DReg)in this work,termed instance-by-instance(IBI).It successively registers instances while systematically reducing outliers,starting from the easiest and progressing to more challenging ones.This enhances the likelihood of effectively registering instances that may have been initially overlooked,allowing for successful registration in subsequent iterations.Under the IBI framework,we further propose a sparse-to-dense correspondence-based multi-instance registration method(IBI-S2DC)to enhance the robustness of MI-3DReg.Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC,e.g.,our mean registration F1 score is 12.02%/12.35%higher than the existing state-of-the-art on the synthetic/real datasets.The source codes are available online at https://github.com/caoxy01/IBI.
文摘OPEN Process Framework(OPF)是使软件开发过程达到CMM5级标准的软件工程框架。文中讨论了基于OPF的软件过程的主要元素及实施过程,并把该过程应用于某油田数据采集系统的开发,实践证明基于OPF的软件过程可以提高团队的开发能力、降低风险、有效控制资源,为项目的开发提供了高度清晰的过程框架,规范管理和开发流程。
文摘为解决国防部体系架构框架(department of defense architecture framework,DoDAF)的视图模型存在动态特性表达困难、决策分析支持缺乏等问题,提出一套体系架构集成迭代设计方法。DoDAF视图模型从多视角整合和表达作战相关信息;ExtendSim可执行模型模拟作战体系架构在多场景下的涌现行为和动态特性;决策模型通过多目标决策规则对架构方案进行量化分析和选择,最终形成“视图-仿真-决策-迭代”的体系架构集成迭代设计方法,并明确架构模型设计流程与模型间数据交互路径。仿真实验验证了其可行性。
基金Hubei Provincial Natural Science Foundation of China,Grant/Award Number:2022CFB555Open Project of State Key Laboratory of New Textile Materials and Advanced Processing Technologies,Grant/Award Number:FZ2021003。
文摘Covalent organic frameworks(COFs),which are constructed by linking organic building blocks via dynamic covalent bonds,are newly emerged and burgeoning crystalline porous copolymers with features including programmable topological architecture,pre-designable periodic skeleton,well-defined micro-/meso-pore,large specific surface area,and customizable electroactive functionality.Those benefits make COFs as promising candidates for advanced electrochemical energy storage.Especially,for now,structure engineering of COFs from multiscale aspects has been conducted to enable optimal overall electrochemical performance in terms of structure durability,electrical conductivity,redox activity,and charge storage.In this review,we give a fundamental and insightful study on the correlations between multi-scale structure engineering and eventual electrochemical properties of COFs,started with introducing their basic chemistries and charge storage principles.The careful discussion on the significant achievements in structure engineering of COFs from linkages,redox sites,polygon skeleton,crystal nanostructures,and composite microstructures,and further their effects on the electrochemical behavior of COFs are presented.Finally,the timely cutting-edge perspectives and in-depth insights into COFbased electrodematerials to rationally screen their electrochemical behaviors for addressing future challenges and implementing electrochemical energy storage applications are proposed.
