To address the challenge of identifying the primary causes of energy consumption fluctuations and accurately assessing the influence of various factors in the converter unit of an iron and steel plant,the focus is pla...To address the challenge of identifying the primary causes of energy consumption fluctuations and accurately assessing the influence of various factors in the converter unit of an iron and steel plant,the focus is placed on the critical components of material and heat balance.Through a thorough analysis of the interactions between various components and energy consumptions,six pivotal factors have been identified—raw material composition,steel type,steel temperature,slag temperature,recycling practices,and operational parameters.Utilizing a framework based on an equivalent energy consumption model,an integrated intelligent diagnostic model has been developed that encapsulates these factors,providing a comprehensive assessment tool for converter energy consumption.Employing the K-means clustering algorithm,historical operational data from the converter have been meticulously analyzed to determine baseline values for essential variables such as energy consumption and recovery rates.Building upon this data-driven foundation,an innovative online system for the intelligent diagnosis of converter energy consumption has been crafted and implemented,enhancing the precision and efficiency of energy management.Upon implementation with energy consumption data at a steel plant in 2023,the diagnostic analysis performed by the system exposed significant variations in energy usage across different converter units.The analysis revealed that the most significant factor influencing the variation in energy consumption for both furnaces was the steel grade,with contributions of−0.550 and 0.379.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering a...In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower.展开更多
在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随...在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随机生成足够长的训练序列,然后将训练序列每一簇的均值作为K-means聚类中心,避免了传统K-means反复迭代寻找聚类中心。进一步,提出了基于神经网络的IC-Kmeans(Neural Network Based IC-Kmeans,NNIC-Kmeans)算法,使用反向传播神经网络将接收端二维数据映射至三维空间,以增加不同簇之间混合数据的距离,提高了分类准确性。蒙特卡罗误码率仿真表明,IC-Kmeans均衡和传统K-means算法的误码率性能相当,但可以显著降低复杂度,特别是在信噪比较小时。同时,在室内多径信道模型下,与IC-Kmeans和传统Kmeans均衡相比,NNIC-Kmeans均衡的光正交频分复用系统误码率性能最好。展开更多
In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared dista...In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation.展开更多
Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction m...Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.展开更多
For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the s...For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.展开更多
A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the a...A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the accuracy of the nominal flight profile,including the nominal altitude profile and the speed profile.First,considering the characteristics of trajectory data,we developed an improved K-means algorithm.The approach was to measure the similarity between different altitude profiles by integrating the space warp edit distance algorithm,thereby to acquire several fitted nominal flight altitude profiles.This approach breaks the constraints of traditional K-means algorithms.Second,to eliminate the influence of meteorological factors,we introduced historical gridded binary data to determine the en-route wind speed and temperature via inverse distance weighted interpolation.Finally,we facilitated the true airspeed determined by speed triangle relationships and the calibrated airspeed determined by aircraft data model to extract a more accurate nominal speed profile from each cluster,therefore we could describe the airspeed profiles above and below the airspeed transition altitude,respectively.Our experimental results showed that the proposed method could obtain a highly accurate nominal flight profile,which reflects the actual aircraft flight status.展开更多
基金financial support from the National Key R&D Program of China(Grant No.2020YFB1711100).
文摘To address the challenge of identifying the primary causes of energy consumption fluctuations and accurately assessing the influence of various factors in the converter unit of an iron and steel plant,the focus is placed on the critical components of material and heat balance.Through a thorough analysis of the interactions between various components and energy consumptions,six pivotal factors have been identified—raw material composition,steel type,steel temperature,slag temperature,recycling practices,and operational parameters.Utilizing a framework based on an equivalent energy consumption model,an integrated intelligent diagnostic model has been developed that encapsulates these factors,providing a comprehensive assessment tool for converter energy consumption.Employing the K-means clustering algorithm,historical operational data from the converter have been meticulously analyzed to determine baseline values for essential variables such as energy consumption and recovery rates.Building upon this data-driven foundation,an innovative online system for the intelligent diagnosis of converter energy consumption has been crafted and implemented,enhancing the precision and efficiency of energy management.Upon implementation with energy consumption data at a steel plant in 2023,the diagnostic analysis performed by the system exposed significant variations in energy usage across different converter units.The analysis revealed that the most significant factor influencing the variation in energy consumption for both furnaces was the steel grade,with contributions of−0.550 and 0.379.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金The National Natural Science Foundation of China(No50674086)Specialized Research Fund for the Doctoral Program of Higher Education (No20060290508)the Youth Scientific Research Foundation of China University of Mining and Technology (No2006A047)
文摘In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower.
文摘在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随机生成足够长的训练序列,然后将训练序列每一簇的均值作为K-means聚类中心,避免了传统K-means反复迭代寻找聚类中心。进一步,提出了基于神经网络的IC-Kmeans(Neural Network Based IC-Kmeans,NNIC-Kmeans)算法,使用反向传播神经网络将接收端二维数据映射至三维空间,以增加不同簇之间混合数据的距离,提高了分类准确性。蒙特卡罗误码率仿真表明,IC-Kmeans均衡和传统K-means算法的误码率性能相当,但可以显著降低复杂度,特别是在信噪比较小时。同时,在室内多径信道模型下,与IC-Kmeans和传统Kmeans均衡相比,NNIC-Kmeans均衡的光正交频分复用系统误码率性能最好。
文摘在高压并联电抗器声纹信号监测系统中,长时海量无标签声纹的高维非平稳性导致特征提取困难、无监督聚类适应性差。由此提出了一种基于深度自适应K-means++算法(deep adaptive K-means++clustering algorithm,DAKCA)的750 kV电抗器声纹聚类方法。首先通过采用两阶段无监督策略微调的改进堆叠稀疏自编码器(stacked sparse autoencoder,SSAE),对快速傅里叶变换后的归一化频域数据提取电抗器原始声纹32维深度特征。进一步提出了依据最近邻聚类有效性指标(clustering validation index based on nearest neighbors,CVNN)的自适应K-means++聚类算法,构建了能自适应确定最优聚类个数的电抗器声纹聚类模型。最后通过西北地区某750 kV电抗器实测声纹数据集进行了验证。结果表明,DAKCA算法对无标签声纹数据在不同样本均衡程度下能够稳定提取32维深度特征,并实现最优聚类,为直接高效利用电抗器无标签声纹数据提供了参考。
文摘In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation.
基金supported by the Beijing Natural Science Foundation,China(4202066)the Central Public-interest Scientific Institution Basal Research Fund,China(JBYWAII-2020-29 and JBYW-AII-2020-31)+1 种基金the Key Research and Development Program of Hebei Province,China(19227407D)the Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences(CAAS-ASTIP2020-All)。
文摘Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.
基金supported in part by the National Natural Science Foundation of China under Grand No.61871129 and No.61301179Projects of Science and Technology Plan Guangdong Province under Grand No.2014A010101284
文摘For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.
基金supported by the National Natural Science Foundation of China(Nos.61174180,U1433125)the Jiangsu Province Science Foundation (No.BK20141413)the Chinese Postdoctoral Science Foundation (No.2014M550291)
文摘A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the accuracy of the nominal flight profile,including the nominal altitude profile and the speed profile.First,considering the characteristics of trajectory data,we developed an improved K-means algorithm.The approach was to measure the similarity between different altitude profiles by integrating the space warp edit distance algorithm,thereby to acquire several fitted nominal flight altitude profiles.This approach breaks the constraints of traditional K-means algorithms.Second,to eliminate the influence of meteorological factors,we introduced historical gridded binary data to determine the en-route wind speed and temperature via inverse distance weighted interpolation.Finally,we facilitated the true airspeed determined by speed triangle relationships and the calibrated airspeed determined by aircraft data model to extract a more accurate nominal speed profile from each cluster,therefore we could describe the airspeed profiles above and below the airspeed transition altitude,respectively.Our experimental results showed that the proposed method could obtain a highly accurate nominal flight profile,which reflects the actual aircraft flight status.
