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SDNMS:A Software Defined Network Measurement System for NFV Networks 预览
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作者 Tianqi Zhang Ming Chen +2 位作者 Xianglin Wei Bing Chen Chao Hu 《中国通信:英文版》 SCIE CSCD 2019年第4期59-74,共16页
As a promising technology to completely transform how we architect,deploy,operate and manage various networks,software-based Network Function Virtualization(NFV)enables hardware-independent,flexible,fast and efficient... As a promising technology to completely transform how we architect,deploy,operate and manage various networks,software-based Network Function Virtualization(NFV)enables hardware-independent,flexible,fast and efficient network service provision.With the increasing popularity of NFV paradigm,the Internet has also transformed to be a hybrid environment where NFV-based network entities coexist with traditional network devices.To facilitate our understanding,design,evaluate and manage of such novel network environment,there is a great need for a new NFV-compatible network measurement system which is still in absent so far.To bridge this gap,a system,named Software Defined Network Measurement System(SDNMS),is presented in this paper.Firstly,the architecture of SDNMS is proposed.In this architecture,a formal method to describe the working mode of the network measurement is defined.This method can also be utilized to generate a network measurement middle box(NMMB)in a specific location of the NFV network according to the customized description file,and to flexibly deploy the network measurement function.Secondly,the technology of virtual network measurement function(VNMF)combined with LXC is studied to form NMMB function.Thirdly,a control method is presented to control the start,stop,and update NMMB to form a specific network measurement system function.Finally,a prototype of SDNMS with network monitoring function to monitor network performance anomalies and locate faults is introduced.Experimental results have shown that SDNMS architecture and related technologies are feasible and effective to deploy and control network measurement functions in NFV networks.We hope SDNMS could provide a new method for practitioners to conduct network management and research at the age of NFV. 展开更多
关键词 NETWORK FUNCTION VIRTUALIZATION NETWORK MEASUREMENT system architecture NETWORK MEASUREMENT FUNCTION service CHAIN
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A New Approach to Multivariate Network Traffic Analysis
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作者 Jinoh Kim Alex Sim 《计算机科学技术学报:英文版》 SCIE EI CSCD 2019年第2期388-402,共15页
Network traffic analysis is one of the core functions in network monitoring for effective network operations and management.While online traffic analysis has been widely studied,it is still intensively challenging due... Network traffic analysis is one of the core functions in network monitoring for effective network operations and management.While online traffic analysis has been widely studied,it is still intensively challenging due to several reasons.One of the primary challenges is the heavy volume of traffic to analyze within a finite amount of time due to the increasing network bandwidth.Another important challenge for effective traffic analysis is to support multivariate functions of traffic variables to help administrators identify unexpected network events intuitively.To this end,we propose a new approach with the multivariate analysis that offers a high-level summary of the online network traffic.With this approach,the current state of the network will display patterns compiled from a set of traffic variables,and the detection problems in network monitoring(e.g.,change detection and anomaly detection)can be reduced to a pattern identification and classification problem.In this paper,we introduce our preliminary work with clustered patterns for online,multivariate network traffic analysis with the challenges and limitations we observed.We then present a grid-based model that is designed to overcome the limitations of the clustered pattern-based technique.We will discuss the potential of the new model with respect to the technical challenges including streaming-based computation and robustness to outliers. 展开更多
关键词 NETWORK TRAFFIC ANALYSIS MULTIVARIATE ANALYSIS time-series SIMILARITY NETWORK MONITORING
Geospatial Data to Images:A Deep-Learning Framework for Traffic Forecasting
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作者 Weiwei Jiang Lin Zhang 《清华大学学报自然科学版(英文版)》 EI CAS CSCD 2019年第1期52-64,共13页
Traffic forecasting has been an active research field in recent decades,and with the development of deep- learning technologies,researchers are trying to utilize deep learning to achieve tremendous improvements in tra... Traffic forecasting has been an active research field in recent decades,and with the development of deep- learning technologies,researchers are trying to utilize deep learning to achieve tremendous improvements in traffic forecasting,as it has been seen in other research areas,such as speech recognition and image classification.In this study,we summarize recent works in which deep-learning methods were applied for geospatial data-based traffic forecasting problems.Based on the insights from previous works,we further propose a deep-learning framework, which transforms geospatial data to images,and then utilizes the state-of-the-art deep-learning methodologies such as Convolutional Neural Network (CNN)and residual networks.To demonstrate the simplicity and effectiveness of our framework,we present a formulation of the New York taxi pick-up/drop-off forecasting problem,and show that our framework significantly outperforms traditional methods,including Historical Average (HA)and AutoRegressive Integrated Moving Average (ARIMA). 展开更多
关键词 GEOSPATIAL data deep LEARNING convolutional NEURAL NETWORK RESIDUAL NETWORK traffic forecasting
Space Efficient Quantization for Deep Convolutional Neural Networks
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作者 Dong-Di Zhao Fan Li +2 位作者 Kashif Sharif Guang-Min Xia Yu Wang 《计算机科学技术学报:英文版》 SCIE EI CSCD 2019年第2期305-317,共13页
Deep convolutional neural networks(DCNNs)have shown outstanding performance in the fields of computer vision,natural language processing,and complex system analysis.With the improvement of performance with deeper laye... Deep convolutional neural networks(DCNNs)have shown outstanding performance in the fields of computer vision,natural language processing,and complex system analysis.With the improvement of performance with deeper layers,DCNNs incur higher computational complexity and larger storage requirement,making it extremely difficult to deploy DCNNs on resource-limited embedded systems(such as mobile devices or Internet of Things devices).Network quantization efficiently reduces storage space required by DCNNs.However,the performance of DCNNs often drops rapidly as the quantization bit reduces.In this article,we propose a space efficient quantization scheme which uses eight or less bits to represent the original 32-bit weights.We adopt singular value decomposition(SVD)method to decrease the parameter size of fully-connected layers for further compression.Additionally,we propose a weight clipping method based on dynamic boundary to improve the performance when using lower precision.Experimental results demonstrate that our approach can achieve up to approximately 14x compression while preserving almost the same accuracy compared with the full-precision models.The proposed weight clipping method can also significantly improve the performance of DCNNs when lower precision is required. 展开更多
关键词 convolutional NEURAL NETWORK MEMORY compression NETWORK QUANTIZATION
特定虚拟操作环境下多机协同算法优化及实现 预览
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作者 蒋成伟 康兴无 +1 位作者 王旭平 陈俊康 《空军工程大学学报:自然科学版》 CSCD 北大核心 2019年第1期26-31,共6页
针对大型复杂导弹武器装备协同操作在同一时空下的特殊情况,开展局域网这个特定虚拟操作环境下的多机(计算机)协同操作研究。通过Unity3D/network技术进行局域网环境的构建,在network模块内置封装好的组件算法之上,通过加入计数器和控... 针对大型复杂导弹武器装备协同操作在同一时空下的特殊情况,开展局域网这个特定虚拟操作环境下的多机(计算机)协同操作研究。通过Unity3D/network技术进行局域网环境的构建,在network模块内置封装好的组件算法之上,通过加入计数器和控制脚本调用实现算法的优化,进而减少多机协同过程中从服务器到客户端的网络延迟。结果证明:基于优化算法基础的多机协同网络延迟较network模块内置封装好的算法表现更好,完全可以满足特定虚拟操作环境下的多机协同操作。 展开更多
关键词 NETWORK 计数器 多机协同 虚拟仿真
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Exploring evolutionary features of directed weighted hazard network in the subway construction
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作者 侯公羽 靳聪 +2 位作者 许哲东 于萍 曹怡怡 《中国物理B:英文版》 SCIE EI CAS CSCD 2019年第3期399-407,共9页
A better understanding of previous accidents is an effective way to reduce the occurrence of similar accidents in the future. In this paper, a complex network approach is adopted to construct a directed weighted hazar... A better understanding of previous accidents is an effective way to reduce the occurrence of similar accidents in the future. In this paper, a complex network approach is adopted to construct a directed weighted hazard network(DWHN) to analyze topological features and evolution of accidents in the subway construction. The nodes are hazards and accidents, the edges are multiple relationships of these nodes and the weight of edges are occurrence times of repetitive relationships. The results indicate that the DWHN possesses the property of small-world with small average path length and large clustering coefficient, indicating that hazards have better connectivity and will spread widely and quickly in the network. Moreover,the DWHN has the property of scale-free network for the cumulative degree distribution follows a power-law distribution.It makes DWHN more vulnerable to target attacks. Controlling key nodes with higher degree, strength and betweenness centrality will destroy the connectivity of DWHN and mitigate the spreading of accidents in the network. This study is helpful for discovering inner relationships and evolutionary features of hazards and accidents in the subway construction. 展开更多
关键词 accident analysis directed WEIGHTED NETWORK complex NETWORK EVOLUTIONARY FEATURES
Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology
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作者 Yufei Liu Yuan Zhou +3 位作者 Xin Liu Fang Dong Chang Wang Zihong Wang 《工程(英文)》 2019年第1期156-163,共8页
It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number o... It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number of labeled samples, which restricts the application of deep learning in an environment with a small sample size. In this paper, we propose an approach based on a generative adversarial network (GAN) combined with a deep neural network (DNN). First, the original samples were divided into a training set and a test set. The GAN was trained with the training set to generate synthetic sample data, which enlarged the training set. Next, the DNN classifier was trained with the synthetic samples. Finally, the classifier was tested with the test set, and the effectiveness of the approach for multi-classification with a small sample size was validated by the indicators. As an empirical case, the approach was then applied to identify the stages of cancers with a small labeled sample size. The experimental results verified that the proposed approach achieved a greater accuracy than traditional methods. This research was an attempt to transform the classical statistical machine-learning classification method based on original samples into a deep-learning classification method based on data augmentation. The use of this approach will contribute to an expansion of application scenarios for the new generation of artificial intelligence based on deep learning, and to an increase in application effectiveness. This research is also expected to contribute to the comprehensive promotion of new-generation artificial intelligence. 展开更多
关键词 Artificial intelligence GENERATIVE adversarial NETWORK Deep neural NETWORK SMALL SAMPLE size CANCER
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Study on optimal state estimation strategy with dual distributed controllers based on Kalman filtering 预览
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作者 陈雅雯 Wang Zhuwei +2 位作者 Fang Chao Xu Guangshu Zhang Yanhua 《高技术通讯:英文版》 CAS 2019年第1期105-110,共6页
Considering dual distributed controllers, a design of optimal state estimation strategy is studied for the wireless sensor and actuator network (WSAN). In particular, the optimal linear quadratic (LQ) control strategy... Considering dual distributed controllers, a design of optimal state estimation strategy is studied for the wireless sensor and actuator network (WSAN). In particular, the optimal linear quadratic (LQ) control strategy with estimated plant state is formulated as a non-cooperative game with network-induced delays. Then, using the Kalman filter approach, an optimal estimation of the plant state is obtained based on the information fusion of the distributed controllers. Finally, an optimal state estimation strategy is derived as a linear function of the current estimated plant state and the last control strategy of multiple controllers. The effectiveness of the proposed closed-loop control strategy is verified by the simulation experiments. 展开更多
关键词 OPTIMAL state estimation STRATEGY wireless SENSOR and ACTUATOR network (WSAN) distributed controllers KALMAN filter network-induced DELAYS
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A PSO Based Multi-Domain Virtual Network Embedding Approach 预览
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作者 Yongjing Ni Guoyan Huang +3 位作者 Sheng Wu Chenxi Li Peiying Zhang Haipeng Yao 《中国通信:英文版》 SCIE CSCD 2019年第4期105-119,共15页
This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture.The local controller first selects candidate substrate nodes for each virtual node in the domain.Then t... This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture.The local controller first selects candidate substrate nodes for each virtual node in the domain.Then the global controller abstracts substrate network topology based on the candidate nodes and boundary nodes of each domain,and applies Particle Swarm Optimization Algorithm on it to divide virtual network requests.Each local controller then embeds the virtual nodes of the divided single-domain virtual network requests in the domain,and cooperates with other local controllers to embed the inter-domain virtual links.Simulation experimental results show that the proposed algorithm has good performance in reducing embedding cost with good stability and scalability. 展开更多
关键词 MULTI-DOMAIN VIRTUAL NETWORK EMBEDDING CANDIDATE node particle swarm optimization algorithm VIRTUAL NETWORK REQUEST division
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A Cost-Efficient Approach to Storing Users'Data for Online Social Networks
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作者 Jing-Ya Zhou Jian-Xi Fan +1 位作者 Cheng-Kuan Lin Bao-Lei Cheng 《计算机科学技术学报:英文版》 SCIE EI CSCD 2019年第1期234-252,共19页
As users increasingly befriend others and interact online via their social media accounts,online social networks (OSNs)are expanding rapidly.Confronted with the big data generated by users,it is imperative that data s... As users increasingly befriend others and interact online via their social media accounts,online social networks (OSNs)are expanding rapidly.Confronted with the big data generated by users,it is imperative that data storage be distributed,scalable,and cost-efficient.Yet one of the most significant challenges about this topic is determining how to minimize the cost without deteriorating system performance.Although many storage systems use the distributed key value store,it cannot be directly applied to OSN storage systems.And because users'data are highly correlated,hash storage leads to frequent inter-server communications,and the high inter-server traffic costs decrease the OSN storage system's scalability. Previous studies proposed conducting network partitioning and data replication based on social graphs.However,data replication increases storage costs and impacts traffic costs.Here,we consider how to minimize costs from the perspective of data storage,by combining partitioning and replication.Our cost-efficient data storage approach supports scalable OSN storage systems.The proposed approach co-locates frequently interactive users together by conducting partitioning and replication simultaneously while meeting load-balancing constraints.Extensive experiments are undertaken on two real- world traces,and the results show that our approach achieves lower cost compared with state-of-the-art approaches.Thus we conclude that our approach enables economic and scalable OSN data storage. 展开更多
关键词 online SOCIAL NETWORK inter-server traffic COST storage COST NETWORK partitioning DATA REPLICATION
Controllability and Its Applications to Biological Networks
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作者 Lin Wu Min Li +1 位作者 Jian-Xin Wang Fang-Xiang Wu 《计算机科学技术学报:英文版》 SCIE EI CSCD 2019年第1期16-34,共19页
Biological elements usually exert their functions through interactions with others to form various types of biological networks.The ability of controlling the dynamics of biological networks is of enormous benefits to... Biological elements usually exert their functions through interactions with others to form various types of biological networks.The ability of controlling the dynamics of biological networks is of enormous benefits to pharmaceutical and medical industry as well as scientific research.Though there are many mathematical methods for steering dynamic systems towards desired states,the methods are usually not feasible for applying to complex biological networks.The difficulties come from the lack of accurate model that can capture the dynamics of interactions between biological elements and the fact that many mathematical methods are computationally intractable for large-scale networks.Recently,a concept in control theory --controllability,has been applied to investigate the dynamics of complex networks.In this article, recent advances on the controllability of complex networks and applications to biological networks are reviewed.Developing dynamic models is the prior concern for analyzing dynamics of biological networks.First,we introduce a widely used dynamic model for investigating controllability of complex networks.Then recent studies of theorems and algorithms for having complex biological networks controllable in general or specific application scenarios are reviewed.Finally,applications to real biological networks manifest that investigating the controllability of biological networks can shed lights on many critical physiological or medical problems,such as revealing biological mechanisms and identifying drug targets,from a systematic perspective. 展开更多
关键词 BIOLOGICAL NETWORK NETWORK CONTROLLABILITY STEERING NODE
Multi-scale object detection by top-down and bottom-up feature pyramid network 预览
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作者 ZHAO Baojun ZHAO Boya +2 位作者 TANG Linbo WANG Wenzheng WU Chen 《系统工程与电子技术:英文版》 SCIE EI CSCD 2019年第1期1-12,共12页
While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection ... While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network (TDBU-FPN), which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes (PASCAL VOC) dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps. 展开更多
关键词 convolutional neural NETWORK (CNN) FEATURE PYRAMID NETWORK (FPN) object detection deconvolution.
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网络电子证据保全公证的相关问题探析 预览
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作者 李颖杰 《中小企业管理与科技》 2019年第2期123-124,共2页
在违法行为、民事诉讼,甚至是刑事犯罪活动中,以网络为依托的电子数据在认定案件事实的过程中起着重要作用,办理网络电子证据的保全公证变得越来越重要。论文旨在梳理挖掘当前我国网络电子证据公证过程中存在的问题,结合实际情况,提出... 在违法行为、民事诉讼,甚至是刑事犯罪活动中,以网络为依托的电子数据在认定案件事实的过程中起着重要作用,办理网络电子证据的保全公证变得越来越重要。论文旨在梳理挖掘当前我国网络电子证据公证过程中存在的问题,结合实际情况,提出相应对策建议。 展开更多
关键词 网络 电子证据 公证
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北京轨道交通线网运营存在问题和对策研究 预览
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作者 杨珂 李祖杰 《山西建筑》 2019年第6期245-246,共2页
对北京轨道交通线网现状进行综述,提出目前运营存在的运能不足问题、线网层次单一问题、断头线单点衔接问题、换乘接驳问题、运营模式等五个主要问题,针对这五个问题逐一进行研究,分析了产生的原因,提出了解决问题的对策,以供其他正在... 对北京轨道交通线网现状进行综述,提出目前运营存在的运能不足问题、线网层次单一问题、断头线单点衔接问题、换乘接驳问题、运营模式等五个主要问题,针对这五个问题逐一进行研究,分析了产生的原因,提出了解决问题的对策,以供其他正在规划建设轨道交通城市参考。 展开更多
关键词 轨道交通 网络化 运营模式
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Optimally Embedding 3-Ary n-Cubes into Grids
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作者 Wei-Bei Fan Jian-Xi Fan +3 位作者 Cheng-Kuan Lin Yan Wang Yue-Juan Han Ru-Chuan Wang 《计算机科学技术学报:英文版》 SCIE EI CSCD 2019年第2期372-387,共16页
The 3-ary n-cube,denoted as Qn3,is an important interconnection network topology proposed for parallel computers,owing to its many desirable properties such as regular and symmetrical structure,and strong scalability,... The 3-ary n-cube,denoted as Qn3,is an important interconnection network topology proposed for parallel computers,owing to its many desirable properties such as regular and symmetrical structure,and strong scalability,among others.In this paper,we first obtain an exact formula for the minimum wirelength to embed Qn3 into grids.We then propose a load balancing algorithm for embedding Qn3 into a square grid with minimum dilation and congestion.Finally,we derive an O(N2)algorithm for embedding Qn3 into a gird with balanced communication,where N is the number of nodes in Qn3.Simulation experiments are performed to verify the total wirelength and evaluate the network cost of our proposed embedding algorithm. 展开更多
关键词 3-ary N-CUBE EMBEDDING algorithm GRID INTERCONNECTION NETWORK
节点增减机制下的病毒传播模型及稳定性
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作者 王刚 胡鑫 陆世伟 《电子科技大学学报》 EI CAS CSCD 北大核心 2019年第1期74-79,共6页
该文针对网络节点的增减情况,研究网络病毒传播模型及其稳定性问题。考虑网络节点的新增和移除,构建了基于节点增减机制下的网络病毒传播模型,并运用Routh-Hurwitz稳定性判据定理,分析了模型的平衡点稳定性和基本再生数R0及其对病毒传... 该文针对网络节点的增减情况,研究网络病毒传播模型及其稳定性问题。考虑网络节点的新增和移除,构建了基于节点增减机制下的网络病毒传播模型,并运用Routh-Hurwitz稳定性判据定理,分析了模型的平衡点稳定性和基本再生数R0及其对病毒传播稳定性的影响。最后,通过改变增加节点数量以及易感状态、感染状态的节点移除率,研究3个参数对病毒传播过程的影响,并给出了仿真验证。仿真结果表明,通过调节网络节点的增减数量,能够控制病毒在网络中的传播。 展开更多
关键词 增减机制 模型 网络 稳定性 病毒传播
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Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model
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作者 Won-Mo Jung In-Soo Park +6 位作者 Ye-Seul Lee Chang-Eop Kim Hyangsook Lee Dae-Hyun Hahm Hi-Joon Park Bo-Hyoung Jang Younbyoung Chae 《医学前沿:英文版》 CAS CSCD 2019年第1期112-120,共9页
Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints.The pattern recognition process that pertains to sympt... Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints.The pattern recognition process that pertains to symptoms and diseases and informs acupuncture treatment in a clinical setting was explored. A total of 232 clinical records were collected using a Charting Language program. The relationship between symptom information and selected acupoints was trained using an artificial neural network (ANN). A total of 11 hidden nodes with the highest average precision score were selected through a tenfold cross-validation. Our ANN model could predict the selected acupoints based on symptom and disease information with an average precision score of 0.865 (precision, 0.911;recall, 0.811). This model is a useful tool for diagnostic classification or pattern recognition and for the prediction and modeling of acupuncture treatment based on clinical data obtained in a real-world setting. The relationship between symptoms and selected acupoints could be systematically characterized through knowledge discovery processes, such as pattern identification. 展开更多
关键词 ACUPUNCTURE INDICATION NEURAL network pattern identification prediction
Interval optimal power flow applied to distribution networks under uncertainty of loads and renewable resources 预览
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作者 Pengwei CHEN Xiangning XIAO Xuhui WANG 《现代电力系统与清洁能源学报(英文)》 CSCD 2019年第1期139-150,共12页
Optimal power flow(OPF) has been used for energy dispatching in active distribution networks.To satisfy constraints fully and achieve strict operational bounds under the uncertainties from loads and sources, this pape... Optimal power flow(OPF) has been used for energy dispatching in active distribution networks.To satisfy constraints fully and achieve strict operational bounds under the uncertainties from loads and sources, this paper derives an interval optimal power flow(I-OPF)method employing affine arithmetic and interval Taylor expansion.An enhanced I-OPF method based on successive linear approximation and second-order cone programming is developed to improve solution accuracy.The proposed methods are benchmarked against Monte Carlo simulation(MCS) and stochastic OPF.Tests on a modified IEEE 33-bus system and a real 113-bus distribution network validate the effectiveness and applicability of the proposed methods. 展开更多
关键词 Active distribution network Optimal power flow INTERVAL UNCERTAINTY AFFINE ARITHMETIC SECOND-ORDER cone programming
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Optimized deployment of a radar network based on an improved firefly algorithm
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作者 Xuc-jun ZHANG Wei JIA +3 位作者 Xiang-min GUAN Guo-qiang XU Jun CHEN Yan-bo ZHU 《信息与电子工程前沿:英文版》 SCIE EI CSCD 2019年第3期425-437,共13页
The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network ... The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm(FA) and four recently proposed FA variants. 展开更多
关键词 IMPROVED FIREFLY algorithm Radar surveillance network DEPLOYMENT optimization Unmanned AERIAL vehicle (UAV) INVASION defense
Integrated Network Pharmacology and Antioxidant Activity-Guided Screen System to Exploring Antioxidants and Quality Markers of Shunaoxin Pills against Chronic Cerebral Ischemia
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作者 Nian-Wei Chang Dan-Dan Cheng +5 位作者 Jia-Nan Ni Ying-Ying Guo Guang-Cui Chu Unchol Kim Min Jiang Gang Bai 《世界中医药杂志:英文版》 2019年第1期1-8,共8页
Objective: The main objective of the study is to screen the quality markers (Q-markers) for relieving oxidative stress damage and against chronic cerebral ischemia in Shunaoxin pills (SNX). Methods: The benefit effect... Objective: The main objective of the study is to screen the quality markers (Q-markers) for relieving oxidative stress damage and against chronic cerebral ischemia in Shunaoxin pills (SNX). Methods: The benefit effect of SNX was evaluated by a rat chronic cerebral ischemia model. The main ingredients of SNX were identified by ultra-performance liquid chromatography-quadrupole time-of-flight, whereas its core targets and pathways around antioxidative stress were predicted by PharmMapper and kyoto encyclopedia of genes and genomes (KEGG) analysis. Moreover, the antioxidants were screened by high-performance liquid chromatography with postcolumn derivatization system and then representative ingredients were verified by cell experiments. Results: SNX could increase expression of catalase and superoxide dismutase (SOD) as well as antagonize oxidative damage in the brain. The effects may be related to three types of antioxidant pathways, including nitrogen metabolism, arachidonic acid metabolism, and the cyclic guanosine mono phosphate-dependent protein kinase (cGMP-PKG) signaling pathway by multiple active comp onents regulate targets. Among them, ferulic acid and ligustilide were shown the key scavenging ability for reactive oxygen free radicals and significantly increased the contents of nitric oxide (NO), NO synthase, and SOD as well as decreased malonaldehyde. Conclusion: The oxidation resistances of biological and chemical processes in SNX to protect against cerebral oxidative stress injury were preliminary revealed by an integrated network phannacology and antioxidant activity-guided screen system. Ferulic acid and ligustilide played a major an tioxidant role that could be used as Q-markers to con trol the quality of SNX. 展开更多
关键词 Antioxidant chronic cerebral ISCHEMIA network PHARMACOLOGY Q-markers Shunaoxin PILLS
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