The authors prove the stability of the rings of highest weight vectors of the action of Om x GLn on the complex polynomial rings on Cm,n. As an application, the structure of the rings for m = 3 is determined.
Van der Pauw＇s function is often used in the measurement of a semiconductor＇s resistivity. However, it is difficult to obtain its value from voltage measurements because it has an implicit form. If it can be express...Van der Pauw＇s function is often used in the measurement of a semiconductor＇s resistivity. However, it is difficult to obtain its value from voltage measurements because it has an implicit form. If it can be expressed as a polynomial, a semiconductor＇s resistivity can be obtained from such measurements. Normally, five orders of the abscissa can provide sufficient precision during the expression of any non-linear function. Therefore, the key is to determine the coefficients of the polynomial. By taking five coefficients as weights to construct a neuronetwork, neurocomputing has been used to solve this problem. Finally, the polynomial expression for van der Pauw＇s function is obtained.展开更多
Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the ...Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms.展开更多
There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because the...There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.展开更多
In this paper, the authors get the Coifman type weighted estimates and weak weighted LlogL estimates for vector-valued generalized commutators of multilinear fractional integral with w ∈ A∞. Furthermore, both the bo...In this paper, the authors get the Coifman type weighted estimates and weak weighted LlogL estimates for vector-valued generalized commutators of multilinear fractional integral with w ∈ A∞. Furthermore, both the boundedness of vector-valued multilinear frac- tional integral and the weak weighted LlogL estimates for vector-valued multilinear fractional integral are also obtained.展开更多
Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. ...Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. However, this method actually obtains the performance by extending dimensions and considering that the text and structural information are one-to-one, which is obviously unreasonable. Regarding this issue, a method by weighting vectors is proposed in this paper. Three methods, negative logarithm, modulus and sigmoid function are used to weight-trained vectors, then recombine the weighted vectors and put them into the SVM classifier for evaluation output. By comparing three different weighting methods, the results showed that using negative logarithm weighting achieved better results than the other two using modulus and sigmoid function weighting, and was superior to directly concatenating vectors in the same dimension.展开更多
A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to ...A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.展开更多
[Objective] The research aimed to assess the water resources carrying capacity in Guizhou Province based on the cosine vector included angle method. [Method] By using the cosine vector included angle method, the index...[Objective] The research aimed to assess the water resources carrying capacity in Guizhou Province based on the cosine vector included angle method. [Method] By using the cosine vector included angle method, the index weight was determined. The projection value of water resources carrying capacity in Guizhou Province was counted by using the multi-objective gray relational projection method. Moreover, the projection value which was counted by the index weight determined by the mean-variance method was as the control. [Result] The projection values which were obtained by two kinds of methods were very close, and the ordering result was consistent. [Conclusion] In the assessment of water resources carrying capacity, it was feasible to use the cosine vector included angle method to determine the index weight.展开更多
光伏发电功率的准确预测对于优化能源管理和电网规划及优化调度具有重要的意义。针对以往光伏发电功率预测方法预测精度不高,传统混合网络模型存在参数选择不确定性和收敛速度较慢的问题,基于历史气象数据和光伏发电数据,提出一种结合...光伏发电功率的准确预测对于优化能源管理和电网规划及优化调度具有重要的意义。针对以往光伏发电功率预测方法预测精度不高,传统混合网络模型存在参数选择不确定性和收敛速度较慢的问题,基于历史气象数据和光伏发电数据,提出一种结合向量加权平均(weighted mean of vectors,INFO)算法、卷积网络(convolutional neural network,CNN)和双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)的光伏发电功率预测方法。首先,选取与光伏发电功率预测相关的多种气象因素,含太阳辐射、温度、湿度、风速、气压等气象参数,并分析它们与光伏发电功率之间的关系,然后使用INFO算法对CNNBiLSTM混合网络预测模型的隐藏层节点数、初始学习率和L2正则化系数进行优化,INFO算法通过自适应调整这些参数,缩短了手动调制参数的时间,提高了超参数设置的精度和效率。实验结果表明,通过INFO算法优化的CNN-BiLSTM混合网络相比传统CNN-BiLSTM混合网络具有更高的预测精度。展开更多
【目的】工业控制系统(industrial control system,ICS)中设备间通信过程高度依赖工控协议来实现,协议安全性对保障ICS稳定运行起到关键作用。漏洞挖掘与入侵检测等作为ICS安全防御体系的核心技术组件,其有效性依赖于对工控协议结构及...【目的】工业控制系统(industrial control system,ICS)中设备间通信过程高度依赖工控协议来实现,协议安全性对保障ICS稳定运行起到关键作用。漏洞挖掘与入侵检测等作为ICS安全防御体系的核心技术组件,其有效性依赖于对工控协议结构及语义功能的精确解析。协议逆向分析作为解析协议结构与语义功能的关键技术,其核心环节语义推断精度直接决定协议理解的准确性。然而,受限于工控协议文档缺失、格式异构性强等现实条件,现有语义推断方法普遍依赖专家经验,存在自动化水平不足、跨协议泛化性能有限等固有瓶颈,难以适应实际工业环境中多源异构协议的高精度解析需求。【方法】为解决上述问题,本文提出mBERT协同多源领域自适应与结构化掩码策略的语义推断方法。通过mBERT模型实现跨协议通用语义表示;利用结合注意力权重与位置编码设计的结构化掩码策略,增强模型对协议结构和语义内在联系的表示能力,提高语义推断方法的自动化程度和效率;利用结合对抗训练的多源领域自适应逐步微调策略,提升模型对多个源协议的语义通用表示能力,增强其在多种工控协议上的适用性,实现关键字语义的有效推断。【结果】在辽宁省石油化工行业信息安全重点实验室的典型能源企业攻防演练靶场中开展实验验证,采集了S7comm、Modbus/TCP和EtherNet/IP三种工控协议数据,并利用协议复杂度评分机制组建训练数据集。结果表明,多源领域自适应逐步微调策略能够显著提升模型性能,将其与结构化掩码策略结合,进一步提高了语义推断精度,且本文方法在精确度、召回率与F_(1)分数指标上均显著优于现有基线方法。【结论】本文提出了mBERT协同多源领域自适应与结构化掩码策略的语义推断方法,在语义推断中采用高维球面映射与多任务损失函数,增强了不同语义类别的区分度与模型对协议语义的深层辨识能力。本文方法不仅显著降低了对人工先验知识的依赖,也提升了语义推断效率与跨协议适用性,为工控协议逆向分析及工业系统安全防护提供了具备理论支撑的新路径。展开更多
基金Project supported by the National Natural Science Foundation of China (No.19901015 and No. 19731004).
文摘The authors prove the stability of the rings of highest weight vectors of the action of Om x GLn on the complex polynomial rings on Cm,n. As an application, the structure of the rings for m = 3 is determined.
文摘Van der Pauw＇s function is often used in the measurement of a semiconductor＇s resistivity. However, it is difficult to obtain its value from voltage measurements because it has an implicit form. If it can be expressed as a polynomial, a semiconductor＇s resistivity can be obtained from such measurements. Normally, five orders of the abscissa can provide sufficient precision during the expression of any non-linear function. Therefore, the key is to determine the coefficients of the polynomial. By taking five coefficients as weights to construct a neuronetwork, neurocomputing has been used to solve this problem. Finally, the polynomial expression for van der Pauw＇s function is obtained.
基金This research is funded by Prince Sattam BinAbdulaziz University,Grant Number IF-PSAU-2021/01/18921.
文摘Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms.
基金Project(61374140)supported by the National Natural Science Foundation of China
文摘There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.
基金Supported by the National Natural Science Foundation of China(11271330,11226104,11226108)the Jiangxi Natural Science Foundation of China(20114BAB211007)the Science Foundation of Jiangxi Education Department(GJJ13703)
文摘In this paper, the authors get the Coifman type weighted estimates and weak weighted LlogL estimates for vector-valued generalized commutators of multilinear fractional integral with w ∈ A∞. Furthermore, both the boundedness of vector-valued multilinear frac- tional integral and the weak weighted LlogL estimates for vector-valued multilinear fractional integral are also obtained.
文摘Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. However, this method actually obtains the performance by extending dimensions and considering that the text and structural information are one-to-one, which is obviously unreasonable. Regarding this issue, a method by weighting vectors is proposed in this paper. Three methods, negative logarithm, modulus and sigmoid function are used to weight-trained vectors, then recombine the weighted vectors and put them into the SVM classifier for evaluation output. By comparing three different weighting methods, the results showed that using negative logarithm weighting achieved better results than the other two using modulus and sigmoid function weighting, and was superior to directly concatenating vectors in the same dimension.
基金Supported by the joint fund of National Natural Science Foundation of China and Civil Aviation Administration Foundation of China(No.U1233201)
文摘A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.
基金Supported by Guizhou Province Science and Technology Fund Item(Guizhou Science Together (2009) 2251)High-level PersonnelSpecial Assistance Fund in Guizhou Province (TZJF (2009) 25)Ministry of Education Science and Technology Research Key Item(210201)~~
文摘[Objective] The research aimed to assess the water resources carrying capacity in Guizhou Province based on the cosine vector included angle method. [Method] By using the cosine vector included angle method, the index weight was determined. The projection value of water resources carrying capacity in Guizhou Province was counted by using the multi-objective gray relational projection method. Moreover, the projection value which was counted by the index weight determined by the mean-variance method was as the control. [Result] The projection values which were obtained by two kinds of methods were very close, and the ordering result was consistent. [Conclusion] In the assessment of water resources carrying capacity, it was feasible to use the cosine vector included angle method to determine the index weight.
文摘光伏发电功率的准确预测对于优化能源管理和电网规划及优化调度具有重要的意义。针对以往光伏发电功率预测方法预测精度不高,传统混合网络模型存在参数选择不确定性和收敛速度较慢的问题,基于历史气象数据和光伏发电数据,提出一种结合向量加权平均(weighted mean of vectors,INFO)算法、卷积网络(convolutional neural network,CNN)和双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)的光伏发电功率预测方法。首先,选取与光伏发电功率预测相关的多种气象因素,含太阳辐射、温度、湿度、风速、气压等气象参数,并分析它们与光伏发电功率之间的关系,然后使用INFO算法对CNNBiLSTM混合网络预测模型的隐藏层节点数、初始学习率和L2正则化系数进行优化,INFO算法通过自适应调整这些参数,缩短了手动调制参数的时间,提高了超参数设置的精度和效率。实验结果表明,通过INFO算法优化的CNN-BiLSTM混合网络相比传统CNN-BiLSTM混合网络具有更高的预测精度。
文摘【目的】工业控制系统(industrial control system,ICS)中设备间通信过程高度依赖工控协议来实现,协议安全性对保障ICS稳定运行起到关键作用。漏洞挖掘与入侵检测等作为ICS安全防御体系的核心技术组件,其有效性依赖于对工控协议结构及语义功能的精确解析。协议逆向分析作为解析协议结构与语义功能的关键技术,其核心环节语义推断精度直接决定协议理解的准确性。然而,受限于工控协议文档缺失、格式异构性强等现实条件,现有语义推断方法普遍依赖专家经验,存在自动化水平不足、跨协议泛化性能有限等固有瓶颈,难以适应实际工业环境中多源异构协议的高精度解析需求。【方法】为解决上述问题,本文提出mBERT协同多源领域自适应与结构化掩码策略的语义推断方法。通过mBERT模型实现跨协议通用语义表示;利用结合注意力权重与位置编码设计的结构化掩码策略,增强模型对协议结构和语义内在联系的表示能力,提高语义推断方法的自动化程度和效率;利用结合对抗训练的多源领域自适应逐步微调策略,提升模型对多个源协议的语义通用表示能力,增强其在多种工控协议上的适用性,实现关键字语义的有效推断。【结果】在辽宁省石油化工行业信息安全重点实验室的典型能源企业攻防演练靶场中开展实验验证,采集了S7comm、Modbus/TCP和EtherNet/IP三种工控协议数据,并利用协议复杂度评分机制组建训练数据集。结果表明,多源领域自适应逐步微调策略能够显著提升模型性能,将其与结构化掩码策略结合,进一步提高了语义推断精度,且本文方法在精确度、召回率与F_(1)分数指标上均显著优于现有基线方法。【结论】本文提出了mBERT协同多源领域自适应与结构化掩码策略的语义推断方法,在语义推断中采用高维球面映射与多任务损失函数,增强了不同语义类别的区分度与模型对协议语义的深层辨识能力。本文方法不仅显著降低了对人工先验知识的依赖,也提升了语义推断效率与跨协议适用性,为工控协议逆向分析及工业系统安全防护提供了具备理论支撑的新路径。
