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Wide dynamic detection range of methane gas based on enhanced cavity absorption spectroscopy 被引量:2
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作者 Yu Wang Bo-Kun Ding +4 位作者 Kun-Yang Wang Jiao-Xu Mei Ze-Lin Han Tu Tan Xiao-Ming Gao 《Chinese Physics B》 SCIE EI CAS CSCD 2022年第4期244-248,共5页
Integrated cavity output spectroscopy(ICOS) is an effective technique in trace gase detection.The strong absorption due to the long optical path of this method makes it challenging in the application scenes that have ... Integrated cavity output spectroscopy(ICOS) is an effective technique in trace gase detection.The strong absorption due to the long optical path of this method makes it challenging in the application scenes that have large gas concentration fluctuation,especially when the gas concentration is high.In this paper,we demonstrate an extension of the dynamic range of ICOS by using a detuned laser combined with an off-axis integrating cavity.With this,we improve the upper limit of the dynamic detection range from 0.1%(1000 ppm) to 20% of the gas concentration.This method provides a way of using ICOS in the applications with unpredictable gas concentrations such as gas leak detection,ocean acidification,carbon sequestration,etc. 展开更多
关键词 integrated cavity output spectroscopy(ICOS) trace gas wide dynamic detection absorption positions
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Dynamic Detection Method of Micro-blog Topic Based on Time Series
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作者 Deyang Zhang Yiliang Han Xiaolong Li 《国际计算机前沿大会会议论文集》 2018年第2期17-17,共1页
关键词 TIME SERIES TOPIC detection dynamic Micro-blog topicSingle-pass
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YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head
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作者 Zhongyuan Zhang Wenqiu Zhu 《Computers, Materials & Continua》 SCIE EI 2024年第5期2547-2563,共17页
Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false... Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method. 展开更多
关键词 Object detection YOLOv8 MULTI-SCALE attention mechanism dynamic detection head
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Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids
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作者 Xinyu Wang Xiangjie Wang +2 位作者 Xiaoyuan Luo Xinping Guan Shuzheng Wang 《Global Energy Interconnection》 EI CSCD 2024年第3期362-376,共15页
Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical a... Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs. 展开更多
关键词 Smart energy grids Cyber-physical system dynamic load altering attacks Attack prediction detection and localization
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A dynamic detection method to improve SLAM performance 被引量:6
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作者 GAN Yu ZHANG Jianhua +1 位作者 CHEN Kaiqi LIU Jialing 《Optoelectronics Letters》 EI 2021年第11期693-698,共6页
Simultaneous localization and mapping(SLAM) technology is a research hotspot in the field of intelligent mobile robot, and many researchers have developed many classic systems in the past few decades. However, most of... Simultaneous localization and mapping(SLAM) technology is a research hotspot in the field of intelligent mobile robot, and many researchers have developed many classic systems in the past few decades. However, most of the existing SLAM methods assume that the environment of the robot is static, which results in the performance of the system being greatly reduced in the dynamic environment. To solve this problem, a new dynamic object detection method based on point cloud motion analysis is proposed and incorporated into ORB-SLAM2. First, the method is regarded as a preprocessing stage, detecting moving objects in the scene, and then removing the moving objects to enhance the performance of the SLAM system. Experiments performed on a public RGB-D dataset show that the motion cancellation method proposed in this paper can effectively improve the performance of ORB-SLAM2 in a highly dynamic environment. 展开更多
关键词 ORB A dynamic detection method to improve SLAM performance
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Extraordinary tunable dynamic range of electrochemical aptasensor for accurate detection of ochratoxin A in food samples 被引量:1
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作者 Lin Cheng Hao Qu +3 位作者 Jun Teng Li Yao Feng Xue Wei Chen 《Food Science and Human Wellness》 SCIE 2017年第2期70-76,共7页
We report the design of a sensitive,electrochemical aptasensor for detection of ochratoxin A(OTA)with an extraordinary tunable dynamic sensing range.This electrochemical aptasensor is constructed based on the target i... We report the design of a sensitive,electrochemical aptasensor for detection of ochratoxin A(OTA)with an extraordinary tunable dynamic sensing range.This electrochemical aptasensor is constructed based on the target induced aptamer-folding detection mechanism and the recognition between OTA and its aptamers results in the conformational change of the aptamer probe and thus signal changes for measurement.The dynamic sensing range of the electrochemical aptasensor is successfully tuned by introduction of free assistant aptamer probes in the sensing system.Our electrochemical aptasensor shows an extraordinary dynamic sensing range of 11-order magnitude of OTA concentration from 10^−8 to 10^2 ng/g.Of great significance,the signal response in all OTA concentration ranges is at the same current scale,demonstrating that our sensing protocol in this research could be applied for accurate detections of OTA in a broad range without using any complicated treatment of signal amplification.Finally,OTA spiked red wine and maize samples in different dynamic sensing ranges are determined with the electrochemical aptasensor under optimized sensing conditions.This tuning strategy of dynamic sensing range may offer a promising platform for electrochemical aptasensor optimizations in practical applications. 展开更多
关键词 Electrochemical aptasensor Tunable dynamic detection range OTA detection Extraordinary dynamic range
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Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel
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作者 Qing Ai Hao Tian +4 位作者 Hui Wang Qing Lang Xingchun Huang Xinghong Jiang Qiang Jing 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1797-1827,共31页
Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient... Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance. 展开更多
关键词 Anomaly detection dynamic predictive model structural health monitoring immersed tunnel LSTM ARIMA
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An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN
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作者 Zhihua Liu Shengquan Liu Jian Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第1期411-433,共23页
Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(... Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection performance.On the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features.To address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion detection.First,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic.Meanwhile,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic.In addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch.Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance. 展开更多
关键词 Intrusion detection industrial internet channel spatial attention multiscale features dynamic fusion multi-output learning strategy
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Dynamic-YOLOX:复杂背景下的苹果叶片病害检测模型 被引量:2
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作者 盛帅 段先华 +1 位作者 胡维康 曹伟杰 《计算机科学与探索》 CSCD 北大核心 2024年第8期2118-2129,共12页
针对目前苹果叶片数据集的叶片病害种类不全以及图片背景单一等问题,构建了复杂背景下包括苹果叶部六种常见病害的苹果叶片病害数据集。针对目前主流苹果叶片病害检测模型检测精度不高、模型复杂和不满足实时监测等问题,提出了一种基于Y... 针对目前苹果叶片数据集的叶片病害种类不全以及图片背景单一等问题,构建了复杂背景下包括苹果叶部六种常见病害的苹果叶片病害数据集。针对目前主流苹果叶片病害检测模型检测精度不高、模型复杂和不满足实时监测等问题,提出了一种基于YOLOX-S(you only look once X-S)改进得到的复杂背景下的苹果叶片病害自适应检测模型Dynamic-YOLOX。设计并使用ECA-SPPFCSPC模块(efficient channel attention cross-stage partial fast spatial pyramid pooling module)更换YOLOX-S模型主干网络尾部Dark5中的空间金字塔池化(SPP)以及跨阶段局部网络(CSPNet)模块来增强模型关注深层语义特征、抑制无用信息的能力,并减少硬件内存开销。设计了动态跨阶段局部网络(ODCSP)模块,并用其更换YOLOX-S模型主干网络中Dark2、Dark3、Dark4部分以及颈部网络中所有的CSPNet模块,使得模型在面对不同输入特征时有更强的自适应性,在减少模型的参数量和计算量的同时提高了模型的平均检测精度。引入Varifocal Loss更换模型中分类置信度损失的BCEWithLogits Loss来提升模型对苹果叶片中密集小目标病害的检测精度。在自制数据集上Dynamic-YOLOX相对原始YOLOX-S模型的mAP提升了4.54个百分点,达到84.63%,同时模型的参数量和计算量分别下降了11.97%和13.45%,检测速度达到44.07 FPS。对比主流苹果叶片病害检测模型,Dynamic-YOLOX具有一定优越性。 展开更多
关键词 苹果叶部病害 目标检测 YOLOX 动态跨阶段局部网络(ODCSP) Varifocal Loss
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Deadlock Detection:Background,Techniques,and Future Improvements
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作者 LU Jiachen NIU Zhi +2 位作者 CHEN Li DONG Luming SHEN Taoli 《ZTE Communications》 2024年第2期71-79,共9页
Deadlock detection is an essential aspect of concurrency control in parallel and distributed systems,as it ensures the efficient utilization of resources and prevents indefinite delays.This paper presents a comprehens... Deadlock detection is an essential aspect of concurrency control in parallel and distributed systems,as it ensures the efficient utilization of resources and prevents indefinite delays.This paper presents a comprehensive analysis of the various deadlock detection techniques,including static and dynamic approaches.We discuss the future improvements associated with deadlock detection and provide a comparative evaluation of these techniques in terms of their accuracy,complexity,and scalability.Furthermore,we outline potential future research directions to improve deadlock detection mechanisms and enhance system performance. 展开更多
关键词 deadlock detection static analysis dynamic analysis
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Machine learning algorithm partially reconfigured on FPGA for an image edge detection system
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作者 Gracieth Cavalcanti Batista Johnny Oberg +3 位作者 Osamu Saotome Haroldo F.de Campos Velho Elcio Hideiti Shiguemori Ingemar Soderquist 《Journal of Electronic Science and Technology》 EI CAS CSCD 2024年第2期48-68,共21页
Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for... Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time. 展开更多
关键词 dynamic partial reconfiguration(DPR) Field programmable gate array(FPGA)implementation Image edge detection Support vector regression(SVR) Unmanned aerial vehicle(UAV) pose estimation
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Visual SLAM in dynamic environments based on object detection 被引量:7
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作者 Yong-bao Ai Ting Rui +4 位作者 Xiao-qiang Yang Jia-lin He Lei Fu Jian-bin Li Ming Lu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2021年第5期1712-1721,共10页
A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the environment.However,moving objects can vastly impair the performance of a VSLAM system which relies on... A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the environment.However,moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption.To cope with this challenging topic,a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed.To reduce the influence of dynamic content,we incorporate the deep-learning-based object detection method in the visual odometry,then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system.Experiment with both on the TUM and KITTI benchmark dataset,as well as in a real-world environment,the results clarify that our method can significantly reduce the tracking error or drift,enhance the robustness,accuracy and stability of the VSLAM system in dynamic scenes. 展开更多
关键词 Visual SLAM Object detection dynamic object probability model dynamic environments
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Dynamic evolutionary community detection algorithms based on the modularity matrix 被引量:2
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作者 陈建芮 洪志敏 +1 位作者 汪丽娜 乌兰 《Chinese Physics B》 SCIE EI CAS CSCD 2014年第11期686-691,共6页
Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according... Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according to our proposed differential equations. In each iteration, the phases of the nodes are controlled by several parameters. It is found that the phases of the nodes are ultimately clustered into several communities after a short period of evolution. They can be adopted to detect the communities successfully. The second differential equation can dynamically adjust several parameters, so it can obtain satisfactory detection results. Simulations on some test networks have verified the efficiency of the presented algorithms. 展开更多
关键词 community detection dynamic evolutionary modularity matrix SYNCHRONIZATION
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Application of Bayesian Dynamic Forecast in Anomaly Detection 被引量:1
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作者 阎慧 曹元大 《Journal of Beijing Institute of Technology》 EI CAS 2005年第1期41-44,共4页
A macroscopical anomaly detection method based on intrusion statistic and Bayesian dynamic forecast is presented. A large number of alert data that cannot be dealt with in time are always aggregated in control centers... A macroscopical anomaly detection method based on intrusion statistic and Bayesian dynamic forecast is presented. A large number of alert data that cannot be dealt with in time are always aggregated in control centers of large-scale intrusion detection systems. In order to improve the efficiency and veracity of intrusion analysis, the intrusion intensity values are picked from alert data and Bayesian dynamic forecast method is used to detect anomaly. The experiments show that the new method is effective on detecting macroscopical anomaly in large-scale intrusion detection systems. 展开更多
关键词 intrusion detection system (IDS) Bayesian dynamic forecast anomaly detection
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Overlapping Community Detection in Dynamic Networks 被引量:3
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作者 Nathan Aston Jacob Hertzler Wei Hu 《Journal of Software Engineering and Applications》 2014年第10期872-882,共11页
Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static com... Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Speaker-Listener Label Propagation Algorithm (SLPA) called SLPA Dynamic (SLPAD). This algorithm, tested on two real dynamic networks, cuts down on the time that it would take SLPA to run, as well as produces similar, and in some cases better, communities. We compared SLPAD to SLPA, LabelRankT, and another algorithm we developed, Dynamic Structural Clustering Algorithm for Networks Overlapping (DSCAN-O), to further test its validity and ability to detect overlapping communities when compared to other community detection algorithms. SLPAD proves to be faster than all of these algorithms, as well as produces communities with just as high modularity for each network. 展开更多
关键词 COMMUNITY detection MODULARITY dynamic Networks OVERLAPPING COMMUNITY detection LABEL PROPAGATION
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Dynamic obstacles detection of tram based on laser radar 被引量:3
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作者 KUANG Wen-zhen WU Meng-luo XU Li 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2018年第4期316-320,共5页
The detection of obstacles in a dynamic environment is a hot and difficult problem.A method of autonomously detecting obstacles based on laser radar is proposed as a safety auxiliary structure of tram.The nearest neig... The detection of obstacles in a dynamic environment is a hot and difficult problem.A method of autonomously detecting obstacles based on laser radar is proposed as a safety auxiliary structure of tram.The nearest neighbor method is used for spatial obstacles clustering from laser radar data.By analyzing the characteristics of obstacles,the types of obstacles are determined by time correlation.Experiments were carried out on the developed unmanned aerial vehicle(UAV),and the experimental results verify the effectiveness of the proposed method. 展开更多
关键词 laser radar TRAM dynamic obstacle detection spatial obstacle clustering time correlation nearest neigbor method
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Dynamic unbalance detection of cardan shaft in high-speed train based on EMD-SVD-NHT 被引量:3
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作者 丁建明 林建辉 +1 位作者 何刘 赵洁 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第6期2149-2157,共9页
Contrary to the aliasing defect between the adjacent intrinsic model functions(IMFs) existing in empirical model decomposition(EMD), a new method of detecting dynamic unbalance with cardan shaft in high-speed train wa... Contrary to the aliasing defect between the adjacent intrinsic model functions(IMFs) existing in empirical model decomposition(EMD), a new method of detecting dynamic unbalance with cardan shaft in high-speed train was proposed by applying the combination between EMD, Hankel matrix, singular value decomposition(SVD) and normalized Hilbert transform(NHT). The vibration signals of gimbal installed base were decomposed through EMD to get different IMFs. The Hankel matrix constructed through the single IMF was orthogonally executed through SVD. The critical singular values were selected to reconstruct vibration signs on the basis of the key stack of singular values. Instantaneous frequencys(IFs) of reconstructed vibration signs were applied to detect dynamic unbalance with shaft and eliminated clutter spectrum caused by the aliasing defect between the adjacent IMFs, which highlighted the failure characteristics. The method was verified by test data in the unbalance condition of dynamic cardan shaft. The results show that the method effectively detects the fault vibration characteristics caused by cardan shaft dynamic unbalance and extracts the nature vibration features. With comparison to the traditional EMD-NHT, clarity and failure characterization force are significantly improved. 展开更多
关键词 cardan shaft empirical model decomposition (EMD) singular value decomposition (SVD) normalized Hilbert transform (NHT) dynamic unbalance detection
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Hierarchical adaptive stereo matching algorithm for obstacle detection with dynamic programming 被引量:1
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作者 Ming BAI Yan ZHUANG Wei WANG 《控制理论与应用(英文版)》 EI 2009年第1期41-47,共7页
An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points (GCPs) is presented. To decrease time complexity without losing matching precision,... An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points (GCPs) is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparity-offset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparity-offset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual support-weight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization. 展开更多
关键词 Stereo matching Ground control points Adaptive weighted aggregation Bidirectional dynamic programming Obstacle detection based on stereo vision
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A High-level Architecture for Intrusion Detection on Heterogeneous Wireless Sensor Networks: Hierarchical, Scalable and Dynamic Reconfigurable 被引量:2
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作者 Hossein Jadidoleslamy 《Wireless Sensor Network》 2011年第7期241-261,共21页
Networks protection against different types of attacks is one of most important posed issue into the network and information security domains. This problem on Wireless Sensor Networks (WSNs), in attention to their spe... Networks protection against different types of attacks is one of most important posed issue into the network and information security domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed solutions to protect Wireless Sensor Networks (WSNs) against different types of intrusions;but no one of them has a comprehensive view to this problem and they are usually designed in single-purpose;but, the proposed design in this paper has been a comprehensive view to this issue by presenting a complete Intrusion Detection Architecture (IDA). The main contribution of this architecture is its hierarchical structure;i.e. it is designed and applicable, in one, two or three levels, consistent to the application domain and its required security level. Focus of this paper is on the clustering WSNs, designing and deploying Sensor-based Intrusion Detection System (SIDS) on sensor nodes, Cluster-based Intrusion Detection System (CIDS) on cluster-heads and Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA) are: static and heterogeneous network, hierarchical, distributed and clustering structure along with clusters' overlapping. Finally, this paper has been designed a questionnaire to verify the proposed idea;then it analyzed and evaluated the acquired results from the questionnaires. 展开更多
关键词 Wireless Sensor Network (WSN) Security INTRUSION detection System (IDS) HIERARCHICAL Distributed SCALABLE dynamic RECONFIGURABLE Attack detection.
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Community Detection in Dynamic Social Networks 被引量:1
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作者 Nathan Aston Wei Hu 《Communications and Network》 2014年第2期124-136,共13页
There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of... There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of these algorithms that detect communities in static networks. To make SCAN more effective for the dynamic social networks that are continually changing their structure, we propose the algorithm DSCAN (Dynamic SCAN) which improves SCAN to allow it to update a local structure in less time than it would to run SCAN on the entire network. We also improve SCAN by removing the need for parameter tuning. DSCAN, tested on real world dynamic networks, performs faster and comparably to SCAN from one timestamp to another, relative to the size of the change. We also devised an approach to genetic algorithms for detecting communities in dynamic social networks, which performs well in speed and modularity. 展开更多
关键词 COMMUNITY detection dynamic SOCIAL NETWORKS DENSITY GENETIC ALGORITHMS
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