针对雷达、微波辐射计及无线通信系统中大阵元最小冗余线性阵列(minimum redundancy linear array,MRLA)的获取难题——计算机长时间运算仅得有限解、易遗漏适配应用的最优阵列,通过分析L上元素最少的受限差基、最大连续基线长为L的MRL...针对雷达、微波辐射计及无线通信系统中大阵元最小冗余线性阵列(minimum redundancy linear array,MRLA)的获取难题——计算机长时间运算仅得有限解、易遗漏适配应用的最优阵列,通过分析L上元素最少的受限差基、最大连续基线长为L的MRLA、长度是L的完美稀疏尺的刻度值、L条边的极小优美图顶点标号各自定义的条件,经循环论证,证明四者数学等价;证明得到:线性阵列成对存在;线性阵列的冗余度≥1,阵元数超过4时冗余度>1;若MRLA最大连续基线长度为L且阵元数为n,则最大连续基线长度为L+1的MRLA阵元数不超过n+1.基于大规模MRLA数据分析,提出假设:冗余度≤1.5的线性阵列可视为MRLA.研究还发现了两类新型线性阵列解析公式,能高效筛选出无穷多的MRLA配置模式(即均为完美稀疏尺的刻度数值),并可根据实际需求灵活设定线性阵列冗余度的筛选阈值,为MRLA的应用和完美稀疏尺的设计提供了理论支撑.展开更多
The topology control strategies of wireless sensor networks are very important for reducing the energy consumption of sensor nodes and prolonging the life-span of networks. In this paper, we put forward a minimum-ener...The topology control strategies of wireless sensor networks are very important for reducing the energy consumption of sensor nodes and prolonging the life-span of networks. In this paper, we put forward a minimum-energy path-preserving topology control （MPTC） algorithm based on a concept of none k-redundant edges. MPTC not only resolves the problem of excessive energy consumption because of the unclosed region in small minimum-energy communication network （SMECN）, but also preserves at least one minimum-energy path between every pair of nodes in a wireless sensor network. We also propose an energy-efficient reconfiguration protocol that maintains the minimum-energy path property in the case where the network topology changes dynamically. Finally, we demonstrate the performance improvements of our algorithm through simulation.展开更多
In order to overcome the shortcomings of the previous obstacle avoidance algorithms,an obstacle avoidance algorithm applicable to multiple mobile obstacles was proposed.The minimum prediction distance between obstacle...In order to overcome the shortcomings of the previous obstacle avoidance algorithms,an obstacle avoidance algorithm applicable to multiple mobile obstacles was proposed.The minimum prediction distance between obstacles and a manipulator was obtained according to the states of obstacles and transformed to escape velocity of the corresponding link of the manipulator.The escape velocity was introduced to the gradient projection method to obtain the joint velocity of the manipulator so as to complete the obstacle avoidance trajectory planning.A7-DOF manipulator was used in the simulation,and the results verified the effectiveness of the algorithm.展开更多
Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection ...Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection systems.The complexity of real-world network data poses significant challenges in detection.Machine learning models have shown promise in detecting APTs;however,their performance often suffers when trained on large datasets with redundant or irrelevant features.This study presents a novel,hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data.It combines Mutual Information(MI),Symmetric Uncertainty(SU)and Minimum Redundancy Maximum Relevance(mRMR)to enhance feature selection.MI and SU assess feature relevance,while mRMR maximises relevance and minimises redundancy,ensuring that the most impactful features are prioritised.This method addresses redundancy among selected features,improving the overall efficiency and effectiveness of the detection model.Experiments on a real-world APT datasets were conducted to evaluate the proposed method.Multiple classifiers including,Random Forest,Support Vector Machine(SVM),Gradient Boosting,and Neural Networks were used to assess classification performance.The results demonstrate that the proposed feature selection method significantly enhances detection accuracy compared to baseline models trained on the full feature set.The Random Forest algorithm achieved the highest performance,with near-perfect accuracy,precision,recall,and F1 scores(99.97%).The proposed adaptive thresholding algorithm within the selection method allows each classifier to benefit from a reduced and optimised feature space,resulting in improved training and predictive performance.This research offers a scalable and classifier-agnostic solution for dimensionality reduction in cybersecurity applications.展开更多
文摘针对雷达、微波辐射计及无线通信系统中大阵元最小冗余线性阵列(minimum redundancy linear array,MRLA)的获取难题——计算机长时间运算仅得有限解、易遗漏适配应用的最优阵列,通过分析L上元素最少的受限差基、最大连续基线长为L的MRLA、长度是L的完美稀疏尺的刻度值、L条边的极小优美图顶点标号各自定义的条件,经循环论证,证明四者数学等价;证明得到:线性阵列成对存在;线性阵列的冗余度≥1,阵元数超过4时冗余度>1;若MRLA最大连续基线长度为L且阵元数为n,则最大连续基线长度为L+1的MRLA阵元数不超过n+1.基于大规模MRLA数据分析,提出假设:冗余度≤1.5的线性阵列可视为MRLA.研究还发现了两类新型线性阵列解析公式,能高效筛选出无穷多的MRLA配置模式(即均为完美稀疏尺的刻度数值),并可根据实际需求灵活设定线性阵列冗余度的筛选阈值,为MRLA的应用和完美稀疏尺的设计提供了理论支撑.
基金supported by by National Natural Science Founda-tion of China (No. 60702055)Program for New Century ExcellentTalents in University (NCET-07-0914)the Science and Technology Research Project of Chongqing Municipal Education Commission of China (KJ070521)
文摘The topology control strategies of wireless sensor networks are very important for reducing the energy consumption of sensor nodes and prolonging the life-span of networks. In this paper, we put forward a minimum-energy path-preserving topology control （MPTC） algorithm based on a concept of none k-redundant edges. MPTC not only resolves the problem of excessive energy consumption because of the unclosed region in small minimum-energy communication network （SMECN）, but also preserves at least one minimum-energy path between every pair of nodes in a wireless sensor network. We also propose an energy-efficient reconfiguration protocol that maintains the minimum-energy path property in the case where the network topology changes dynamically. Finally, we demonstrate the performance improvements of our algorithm through simulation.
基金Supported by Ministeral Level Advanced Research Foundation(65822576)Beijing Municipal Education Commission(KM201310858004,KM201310858001)
文摘In order to overcome the shortcomings of the previous obstacle avoidance algorithms,an obstacle avoidance algorithm applicable to multiple mobile obstacles was proposed.The minimum prediction distance between obstacles and a manipulator was obtained according to the states of obstacles and transformed to escape velocity of the corresponding link of the manipulator.The escape velocity was introduced to the gradient projection method to obtain the joint velocity of the manipulator so as to complete the obstacle avoidance trajectory planning.A7-DOF manipulator was used in the simulation,and the results verified the effectiveness of the algorithm.
文摘由于具有高时间分辨率、无创性,脑电(Electroencephalogram,EEG)信号被广泛应用于航空航天任务操作员的疲劳、脑力负荷分析等。针对EEG信号多通道且各通道内信息不完全相同的特性,提出了一种基于最小冗余最大相关性(Minimum redundancy maximum relevance,mRMR)算法的EEG特征评价技术。通过设置目标变量,计算各通道内EEG特征与目标变量的互信息量、特征在通道内部的冗余度,可对EEG特征的性能做出评价。进一步,获取管制员在不同脑力负荷下的EEG数据,对一系列EEG特征做出评价并与已有研究、特征在不同分类方式下的可分性进行对比,验证了该特征评价技术的有效性。与现有的技术相比,该技术避免了灰色关联分析法确定权重参数和灰色关联度的主观性、避免了分类器评价法的差异性。相较于已有的特征选择算法,考虑了通道内部信息的冗余,使得评价结果更为准确。相较于基于统计学的相关技术,该方法可对特征的性能做出定量的评价,以便对不同指标进行比较。最后,阐述了该评价方式疲劳程度分析、情绪识别等方面的应用。
基金funded by Universiti Teknologi Malaysia under the UTM RA ICONIC Grant(Q.J130000.4351.09G61).
文摘Advanced Persistent Threats(APTs)represent one of the most complex and dangerous categories of cyber-attacks characterised by their stealthy behaviour,long-term persistence,and ability to bypass traditional detection systems.The complexity of real-world network data poses significant challenges in detection.Machine learning models have shown promise in detecting APTs;however,their performance often suffers when trained on large datasets with redundant or irrelevant features.This study presents a novel,hybrid feature selection method designed to improve APT detection by reducing dimensionality while preserving the informative characteristics of the data.It combines Mutual Information(MI),Symmetric Uncertainty(SU)and Minimum Redundancy Maximum Relevance(mRMR)to enhance feature selection.MI and SU assess feature relevance,while mRMR maximises relevance and minimises redundancy,ensuring that the most impactful features are prioritised.This method addresses redundancy among selected features,improving the overall efficiency and effectiveness of the detection model.Experiments on a real-world APT datasets were conducted to evaluate the proposed method.Multiple classifiers including,Random Forest,Support Vector Machine(SVM),Gradient Boosting,and Neural Networks were used to assess classification performance.The results demonstrate that the proposed feature selection method significantly enhances detection accuracy compared to baseline models trained on the full feature set.The Random Forest algorithm achieved the highest performance,with near-perfect accuracy,precision,recall,and F1 scores(99.97%).The proposed adaptive thresholding algorithm within the selection method allows each classifier to benefit from a reduced and optimised feature space,resulting in improved training and predictive performance.This research offers a scalable and classifier-agnostic solution for dimensionality reduction in cybersecurity applications.
