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
Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as ...Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.展开更多
Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected ...Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected from rock outcrops.In response,we propose a workflow that balances accuracy and efficiency to extract discontinuities from massive point clouds.The proposed method employs voxel filtering to downsample point clouds,constructs a point cloud topology using K-d trees,utilizes principal component analysis to calculate the point cloud normals,and employs the pointwise clustering(PWC)algorithm to extract discontinuities from rock outcrop point clouds.This method provides information on the location and orientation(dip direction and dip angle)of the discontinuities,and the modified whale optimization algorithm(MWOA)is utilized to identify major discontinuity sets and their average orientations.Performance evaluations based on three real cases demonstrate that the proposed method significantly reduces computational time costs without sacrificing accuracy.In particular,the method yields more reasonable extraction results for discontinuities with certain undulations.The presented approach offers a novel tool for efficiently extracting discontinuities from large-scale point clouds.展开更多
Wireless sensor networks consist of hundreds or thousands of sensor nodes that involve numerous restrictions in-cluding computation capability and battery capacity.Topology control is an important issue for achieving ...Wireless sensor networks consist of hundreds or thousands of sensor nodes that involve numerous restrictions in-cluding computation capability and battery capacity.Topology control is an important issue for achieving a balanced placement of sensor nodes.The clustering scheme is a widely known and efficient means of topology control for transmitting information to the base station in two hops.The automatic routing scheme of the self-organizing technique is another critical element of wireless sensor networks.In this paper we propose an optimal algorithm with cluster balance taken into consideration,and compare it with three well known and widely used approaches,i.e.,LEACH,MEER,and VAP-E,in performance evaluation.Experimental results show that the proposed approach increases the overall network lifetime,indicating that the amount of energy required for com-munication to the base station will be reduced for locating an optimal cluster.展开更多
With the rapid advancement of the Internet,network attack methods are constantly evolving and adapting.To better identify the network attack behavior,a universal gravitation clustering algorithm was proposed by analyz...With the rapid advancement of the Internet,network attack methods are constantly evolving and adapting.To better identify the network attack behavior,a universal gravitation clustering algorithm was proposed by analyzing the dissimilarities and similarities of the clustering algorithms.First,the algorithm designated the cluster set as vacant,with the introduction of a new object.Subsequently,a new cluster based on the given object was constructed.The dissimilarities between it and each existing cluster were calculated using a defined difference measure.Theminimumdissimilaritywas selected.Through comparing the proposed algorithmwith the traditional Back Propagation(BP)neural network and nearest neighbor detection algorithm,the application of the Defense Advanced Research Projects Agency(DARPA)00 and Knowledge Discovery and Data Mining(KDD)Cup 99 datasets revealed that the performance of the proposed algorithmsurpassed that of both algorithms in terms of the detection rate,speed,false positive rate,and false negative rate.展开更多
In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising...In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.展开更多
Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster informa...Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster information.This article focuses on color planar raster geological map(geological maps include planar geological maps,columnar maps,and profiles).While existing deep learning approaches are often used to segment general images,their performance is limited due to complex elements,diverse regional features,and complicated backgrounds for color geological map in the domain of geoscience.To address the issue,a color geological map segmentation model is proposed that combines the Felz clustering algorithm and an improved SE-UNet deep learning network(named GeoMSeg).Firstly,a symmetrical encoder-decoder structure backbone network based on UNet is constructed,and the channel attention mechanism SENet has been incorporated to augment the network’s capacity for feature representation,enabling the model to purposefully extract map information.The SE-UNet network is employed for feature extraction from the geological map and obtain coarse segmentation results.Secondly,the Felz clustering algorithm is used for super pixel pre-segmentation of geological maps.The coarse segmentation results are refined and modified based on the super pixel pre-segmentation results to obtain the final segmentation results.This study applies GeoMSeg to the constructed dataset,and the experimental results show that the algorithm proposed in this paper has superior performance compared to other mainstream map segmentation models,with an accuracy of 91.89%and a MIoU of 71.91%.展开更多
The characterization and clustering of rock discontinuity sets are a crucial and challenging task in rock mechanics and geotechnical engineering.Over the past few decades,the clustering of discontinuity sets has under...The characterization and clustering of rock discontinuity sets are a crucial and challenging task in rock mechanics and geotechnical engineering.Over the past few decades,the clustering of discontinuity sets has undergone rapid and remarkable development.However,there is no relevant literature summarizing these achievements,and this paper attempts to elaborate on the current status and prospects in this field.Specifically,this review aims to discuss the development process of clustering methods for discontinuity sets and the state-of-the-art relevant algorithms.First,we introduce the importance of discontinuity clustering analysis and follow the comprehensive characterization approaches of discontinuity data.A bibliometric analysis is subsequently conducted to clarify the current status and development characteristics of the clustering of discontinuity sets.The methods for the clustering analysis of rock discontinuities are reviewed in terms of single-and multi-parameter clustering methods.Single-parameter methods can be classified into empirical judgment methods,dynamic clustering methods,relative static clustering methods,and static clustering methods,reflecting the continuous optimization and improvement of clustering algorithms.Moreover,this paper compares the current mainstream of single-parameter clustering methods with multi-parameter clustering methods.It is emphasized that the current single-parameter clustering methods have reached their performance limits,with little room for improvement,and that there is a need to extend the study of multi-parameter clustering methods.Finally,several suggestions are offered for future research on the clustering of discontinuity sets.展开更多
Due to global warming, extreme weather and climate events are becoming more frequent, highlighting the need to explore the changing characteristics of precipitation in China, including extreme precipitation. A cluster...Due to global warming, extreme weather and climate events are becoming more frequent, highlighting the need to explore the changing characteristics of precipitation in China, including extreme precipitation. A clustering algorithm was developed to classify summer(June, July, and August) daily precipitation in China from 1961 to 2020, considering spatial distribution, standard deviations, and frequency of extreme precipitation events. The results reveal six distinct precipitation climate zones, a classification that differs from previous divisions. While overall precipitation has decreased in most regions, the frequency of extreme precipitation events has increased across all clusters, indicating a shift in precipitation distribution patterns. Analysis shows that the weakened Lake Baikal blocking high and strengthened Mongolian cyclone influence the arid region in northwest China(Cluster 1), which is characterized by the lowest precipitation.The transition zone between the monsoon and arid region(Cluster 2) is affected by the Mongolian cyclone, water vapor transport from the Indian Ocean, and shifts in the monsoon boundary. Clusters 3 and 4 represent areas associated with advancement and retreat of the summer monsoon. In the Meiyu region, two distinct subregions have been identified exist.Cluster 4 is primarily influenced by the East Asia-Pacific wave train. Despite sharing similar climate drivers and proximity,Clusters 4 and 5 differ significantly due to topographic variations and disparate levels of urbanization. Cluster 5 exhibits a higher average precipitation, greater variability, and more frequent extreme events. Cluster 6 exhibits the highest overall precipitation in the coastal areas of Guangdong and Guangxi, where abundant water vapor contributes to a higher frequency of extreme precipitation. In addition, anthropogenic activities and urbanization significantly influence precipitation in Beijing-Tianjin-Hebei and Yangtze River Delta regions. This research proposes a precipitation classification scheme integrating multiple precipitation parameters, providing support for risk management and mitigation strategies in the face of increasing extreme precipitation events.展开更多
With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have ev...With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source,multi-load systems.This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches-primarily focused on economic objectives-insufficient to meet the growing demands for flexible scheduling and dynamic response.To address these challenges,this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational requirements of distribution networks with a high penetration of new-type source-loads.The goal is to establish a comprehensive energy management framework that optimally balances energy efficiency,carbon reduction,and economic performance in modern distribution networks.To enhance classification accuracy,the strategy constructs amulti-dimensional scenario classification model that integrates environmental and climatic factors by analyzing the operational characteristics of new-type distribution networks and incorporating expert knowledge.An improved split-coupling K-means preclustering algorithm is employed to classify distribution networks effectively.Based on the classification results,fuzzy logic control is then utilized to dynamically optimize the weighting of each objective,allowing for an adaptive adjustment of priorities to achieve a flexible and responsivemulti-objective energy management strategy.The effectiveness of the proposed approach is validated through practical case studies.Simulation results indicate that the proposed method improves classification accuracy by 18.18%compared to traditional classification methods and enhances energy savings and carbon reduction by 4.34%and 20.94%,respectively,compared to the fixed-weight strategy.展开更多
Three-dimensional ocean subsurface temperature and salinity structures(OST/OSS)in the South China Sea(SCS)play crucial roles in oceanic climate research and disaster mitigation.Traditionally,real-time OST and OSS are ...Three-dimensional ocean subsurface temperature and salinity structures(OST/OSS)in the South China Sea(SCS)play crucial roles in oceanic climate research and disaster mitigation.Traditionally,real-time OST and OSS are mainly obtained through in-situ ocean observations and simulation by ocean circulation models,which are usually challenging and costly.Recently,dynamical,statistical,or machine learning models have been proposed to invert the OST/OSS from sea surface information;however,these models mainly focused on the inversion of monthly OST and OSS.To address this issue,we apply clustering algorithms and employ a stacking strategy to ensemble three models(XGBoost,Random Forest,and LightGBM)to invert the real-time OST/OSS based on satellite-derived data and the Argo dataset.Subsequently,a fusion of temperature and salinity is employed to reconstruct OST and OSS.In the validation dataset,the depth-averaged Correlation(Corr)of the estimated OST(OSS)is 0.919(0.83),and the average Root-Mean-Square Error(RMSE)is0.639°C(0.087 psu),with a depth-averaged coefficient of determination(R~2)of 0.84(0.68).Notably,at the thermocline where the base models exhibit their maximum error,the stacking-based fusion model exhibited significant performance enhancement,with a maximum enhancement in OST and OSS inversion exceeding 10%.We further found that the estimated OST and OSS exhibit good agreement with the HYbrid Coordinate Ocean Model(HYCOM)data and BOA_Argo dataset during the passage of a mesoscale eddy.This study shows that the proposed model can effectively invert the real-time OST and OSS,potentially enhancing the understanding of multi-scale oceanic processes in the SCS.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
A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clo...A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clouds in different developmental phases,two-dimensional(2D)and three-dimensional(3D)models are proposed by applying reflectivity factors at 0.5°and at 0.5°,1.5°,and 2.4°elevation angles,respectively.According to the thresholds of the algorithm,which include echo intensity,the echo top height of 35 dBZ(ET),density threshold,andεneighborhood,cloud clusters can be marked into four types:deep-convective cloud(DCC),shallow-convective cloud(SCC),hybrid convective-stratiform cloud(HCS),and stratiform cloud(SFC)types.Each cloud cluster type is further identified as a core area and boundary area,which can provide more abundant cloud structure information.The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing,Xuzhou,and Qingdao.The results show that cloud clusters can be intuitively identified as core and boundary points,which change in area continuously during the process of convective evolution,by the improved DBSCAN algorithm.Therefore,the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification.Because density thresholds are different and multiple elevations are utilized in the 3D model,the identified echo types and areas are dissimilar between the 2D and 3D models.The 3D model identifies larger convective and stratiform clouds than the 2D model.However,the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds.In addition,the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.展开更多
To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is ...To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） 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. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.展开更多
To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used....To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.展开更多
A priori knowledge of the number of tags is crucial for anti-collision protocols in slotted UHF RFID systems.The number of tags is used to decide optimal frame length in dynamic frame slotted ALOHA(DFSA)and to adjust ...A priori knowledge of the number of tags is crucial for anti-collision protocols in slotted UHF RFID systems.The number of tags is used to decide optimal frame length in dynamic frame slotted ALOHA(DFSA)and to adjust access probability in random access protocols.Conventional researches estimate the number of tags in MAC layer based on statistics of empty slots,collided slots and successful slots.Usually,a collision detection algorithm is employed to determine types of time slots.Only three types are distinguished because of lack of ability to detect the number of tags in single time slot.In this paper,a physical layer algorithm is proposed to detect the number of tags in a collided slot.Mean shift algorithm is utilized,and some properties of backscatter signals are investigated.Simulation results verify the effectiveness of the proposed solution in terms of low estimation error with a high SNR range,outperforming the existing MAC layer approaches.展开更多
The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind powe...The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind power at each moment;however,they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency.To resolve this problem,a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed.First,a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented.Second,considering the typical daily output processes as status variables,a wind power time series simulation model based on Markov algorithm is constructed.Finally,a case is analyzed based on the measured data of a wind farm in China.The proposed model is then compared with traditional methods to verify its effectiveness and applicability.The comparison results indicate that the statistical characteristics,probability distributions,and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods.Moreover,modeling efficiency considerably improves.展开更多
A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering ...A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering spatial mapping.First,the clustering sample space was mapped from the image pixels to the quantized color space,and several methods were adopted to compress the amount of clustering samples.Then,an improved pedigree clustering algorithm was applied to obtain the initial class centers.Finally,CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image.After theoretical analysis of the effect and efficiency of the CIQFCM algorithm,several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm.The results indicated that the value of quantization intervals should be set to 4,and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect.In addition,as the image size increased from 128×128 to 1024×1024,the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times,which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.展开更多
DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the alg...DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.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funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region:No.22D01B148Bidding Topics for the Center for Integration of Education and Production and Development of New Business in 2024:No.2024-KYJD05+1 种基金Basic Scientific Research Business Fee Project of Colleges and Universities in Autonomous Region:No.XJEDU2025P126Xinjiang College of Science&Technology School-level Scientific Research Fund Project:No.2024-KYTD01.
文摘Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.
基金supported by the National Natural Science Foundation of China(Grant No.42407232)the Sichuan Science and Technology Program(Grant No.2024NSFSC0826).
文摘Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected from rock outcrops.In response,we propose a workflow that balances accuracy and efficiency to extract discontinuities from massive point clouds.The proposed method employs voxel filtering to downsample point clouds,constructs a point cloud topology using K-d trees,utilizes principal component analysis to calculate the point cloud normals,and employs the pointwise clustering(PWC)algorithm to extract discontinuities from rock outcrop point clouds.This method provides information on the location and orientation(dip direction and dip angle)of the discontinuities,and the modified whale optimization algorithm(MWOA)is utilized to identify major discontinuity sets and their average orientations.Performance evaluations based on three real cases demonstrate that the proposed method significantly reduces computational time costs without sacrificing accuracy.In particular,the method yields more reasonable extraction results for discontinuities with certain undulations.The presented approach offers a novel tool for efficiently extracting discontinuities from large-scale point clouds.
基金supported by the Chung-Ang University Research Scholarship Grants,Korea
文摘Wireless sensor networks consist of hundreds or thousands of sensor nodes that involve numerous restrictions in-cluding computation capability and battery capacity.Topology control is an important issue for achieving a balanced placement of sensor nodes.The clustering scheme is a widely known and efficient means of topology control for transmitting information to the base station in two hops.The automatic routing scheme of the self-organizing technique is another critical element of wireless sensor networks.In this paper we propose an optimal algorithm with cluster balance taken into consideration,and compare it with three well known and widely used approaches,i.e.,LEACH,MEER,and VAP-E,in performance evaluation.Experimental results show that the proposed approach increases the overall network lifetime,indicating that the amount of energy required for com-munication to the base station will be reduced for locating an optimal cluster.
基金supported by the Fujian China University Education Informatization Project(FJGX2023013)National Natural Science Foundation of China Youth Program(72001126)+1 种基金Sanming University’s Research and Optimization of the Function of Safety Test Management and Control Platform Project(KH22097)Young and Middle-Aged Teacher Education Research Project of Fujian Provincial Department of Education(JAT200642,B202033).
文摘With the rapid advancement of the Internet,network attack methods are constantly evolving and adapting.To better identify the network attack behavior,a universal gravitation clustering algorithm was proposed by analyzing the dissimilarities and similarities of the clustering algorithms.First,the algorithm designated the cluster set as vacant,with the introduction of a new object.Subsequently,a new cluster based on the given object was constructed.The dissimilarities between it and each existing cluster were calculated using a defined difference measure.Theminimumdissimilaritywas selected.Through comparing the proposed algorithmwith the traditional Back Propagation(BP)neural network and nearest neighbor detection algorithm,the application of the Defense Advanced Research Projects Agency(DARPA)00 and Knowledge Discovery and Data Mining(KDD)Cup 99 datasets revealed that the performance of the proposed algorithmsurpassed that of both algorithms in terms of the detection rate,speed,false positive rate,and false negative rate.
基金The National Natural Science Foundation of China(No.50674086)Specialized Research Fund for the Doctoral Program of Higher Education(No.20060290508)the Postdoctoral Scientific Program of Jiangsu Province(No.0701045B)
文摘In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.
基金financially supported by the Natural Science Foundation of China(42301492)the Open Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering(2022SDSJ04,2024SDSJ03)+1 种基金the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(GLAB 2023ZR01,GLAB2024ZR08)the Fundamental Research Funds for the Central Universities.
文摘Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster information.This article focuses on color planar raster geological map(geological maps include planar geological maps,columnar maps,and profiles).While existing deep learning approaches are often used to segment general images,their performance is limited due to complex elements,diverse regional features,and complicated backgrounds for color geological map in the domain of geoscience.To address the issue,a color geological map segmentation model is proposed that combines the Felz clustering algorithm and an improved SE-UNet deep learning network(named GeoMSeg).Firstly,a symmetrical encoder-decoder structure backbone network based on UNet is constructed,and the channel attention mechanism SENet has been incorporated to augment the network’s capacity for feature representation,enabling the model to purposefully extract map information.The SE-UNet network is employed for feature extraction from the geological map and obtain coarse segmentation results.Secondly,the Felz clustering algorithm is used for super pixel pre-segmentation of geological maps.The coarse segmentation results are refined and modified based on the super pixel pre-segmentation results to obtain the final segmentation results.This study applies GeoMSeg to the constructed dataset,and the experimental results show that the algorithm proposed in this paper has superior performance compared to other mainstream map segmentation models,with an accuracy of 91.89%and a MIoU of 71.91%.
基金funding support from the National Natural Science Foundation of China(Grant No.42007269)the Young Talent Fund of Xi'an Association for Science and Technology(Grant No.959202313094)the Fundamental Research Funds for the Central Universities,CHD(Grant No.300102263401).
文摘The characterization and clustering of rock discontinuity sets are a crucial and challenging task in rock mechanics and geotechnical engineering.Over the past few decades,the clustering of discontinuity sets has undergone rapid and remarkable development.However,there is no relevant literature summarizing these achievements,and this paper attempts to elaborate on the current status and prospects in this field.Specifically,this review aims to discuss the development process of clustering methods for discontinuity sets and the state-of-the-art relevant algorithms.First,we introduce the importance of discontinuity clustering analysis and follow the comprehensive characterization approaches of discontinuity data.A bibliometric analysis is subsequently conducted to clarify the current status and development characteristics of the clustering of discontinuity sets.The methods for the clustering analysis of rock discontinuities are reviewed in terms of single-and multi-parameter clustering methods.Single-parameter methods can be classified into empirical judgment methods,dynamic clustering methods,relative static clustering methods,and static clustering methods,reflecting the continuous optimization and improvement of clustering algorithms.Moreover,this paper compares the current mainstream of single-parameter clustering methods with multi-parameter clustering methods.It is emphasized that the current single-parameter clustering methods have reached their performance limits,with little room for improvement,and that there is a need to extend the study of multi-parameter clustering methods.Finally,several suggestions are offered for future research on the clustering of discontinuity sets.
基金National Natural Science Foundation of China(U2442202, 42274217, 62441501)Key Innovation Team of China Meteorological Administration (CMA2024ZD01)Scientific Research Foundation of CUIT (376278, KYTZ202158)。
文摘Due to global warming, extreme weather and climate events are becoming more frequent, highlighting the need to explore the changing characteristics of precipitation in China, including extreme precipitation. A clustering algorithm was developed to classify summer(June, July, and August) daily precipitation in China from 1961 to 2020, considering spatial distribution, standard deviations, and frequency of extreme precipitation events. The results reveal six distinct precipitation climate zones, a classification that differs from previous divisions. While overall precipitation has decreased in most regions, the frequency of extreme precipitation events has increased across all clusters, indicating a shift in precipitation distribution patterns. Analysis shows that the weakened Lake Baikal blocking high and strengthened Mongolian cyclone influence the arid region in northwest China(Cluster 1), which is characterized by the lowest precipitation.The transition zone between the monsoon and arid region(Cluster 2) is affected by the Mongolian cyclone, water vapor transport from the Indian Ocean, and shifts in the monsoon boundary. Clusters 3 and 4 represent areas associated with advancement and retreat of the summer monsoon. In the Meiyu region, two distinct subregions have been identified exist.Cluster 4 is primarily influenced by the East Asia-Pacific wave train. Despite sharing similar climate drivers and proximity,Clusters 4 and 5 differ significantly due to topographic variations and disparate levels of urbanization. Cluster 5 exhibits a higher average precipitation, greater variability, and more frequent extreme events. Cluster 6 exhibits the highest overall precipitation in the coastal areas of Guangdong and Guangxi, where abundant water vapor contributes to a higher frequency of extreme precipitation. In addition, anthropogenic activities and urbanization significantly influence precipitation in Beijing-Tianjin-Hebei and Yangtze River Delta regions. This research proposes a precipitation classification scheme integrating multiple precipitation parameters, providing support for risk management and mitigation strategies in the face of increasing extreme precipitation events.
基金supported by the Science and Technology Project of the Headquarters of the State Grid Corporation(project code:5400-202323233A-1-1-ZN).
文摘With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source,multi-load systems.This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches-primarily focused on economic objectives-insufficient to meet the growing demands for flexible scheduling and dynamic response.To address these challenges,this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational requirements of distribution networks with a high penetration of new-type source-loads.The goal is to establish a comprehensive energy management framework that optimally balances energy efficiency,carbon reduction,and economic performance in modern distribution networks.To enhance classification accuracy,the strategy constructs amulti-dimensional scenario classification model that integrates environmental and climatic factors by analyzing the operational characteristics of new-type distribution networks and incorporating expert knowledge.An improved split-coupling K-means preclustering algorithm is employed to classify distribution networks effectively.Based on the classification results,fuzzy logic control is then utilized to dynamically optimize the weighting of each objective,allowing for an adaptive adjustment of priorities to achieve a flexible and responsivemulti-objective energy management strategy.The effectiveness of the proposed approach is validated through practical case studies.Simulation results indicate that the proposed method improves classification accuracy by 18.18%compared to traditional classification methods and enhances energy savings and carbon reduction by 4.34%and 20.94%,respectively,compared to the fixed-weight strategy.
基金jointly supported by the National Key Research and Development Program of China(2022YFC3104304)the National Natural Science Foundation of China(Grant No.41876011)+1 种基金the 2022 Research Program of Sanya Yazhou Bay Science and Technology City(SKJC-2022-01-001)the Hainan Province Science and Technology Special Fund(ZDYF2021SHFZ265)。
文摘Three-dimensional ocean subsurface temperature and salinity structures(OST/OSS)in the South China Sea(SCS)play crucial roles in oceanic climate research and disaster mitigation.Traditionally,real-time OST and OSS are mainly obtained through in-situ ocean observations and simulation by ocean circulation models,which are usually challenging and costly.Recently,dynamical,statistical,or machine learning models have been proposed to invert the OST/OSS from sea surface information;however,these models mainly focused on the inversion of monthly OST and OSS.To address this issue,we apply clustering algorithms and employ a stacking strategy to ensemble three models(XGBoost,Random Forest,and LightGBM)to invert the real-time OST/OSS based on satellite-derived data and the Argo dataset.Subsequently,a fusion of temperature and salinity is employed to reconstruct OST and OSS.In the validation dataset,the depth-averaged Correlation(Corr)of the estimated OST(OSS)is 0.919(0.83),and the average Root-Mean-Square Error(RMSE)is0.639°C(0.087 psu),with a depth-averaged coefficient of determination(R~2)of 0.84(0.68).Notably,at the thermocline where the base models exhibit their maximum error,the stacking-based fusion model exhibited significant performance enhancement,with a maximum enhancement in OST and OSS inversion exceeding 10%.We further found that the estimated OST and OSS exhibit good agreement with the HYbrid Coordinate Ocean Model(HYCOM)data and BOA_Argo dataset during the passage of a mesoscale eddy.This study shows that the proposed model can effectively invert the real-time OST and OSS,potentially enhancing the understanding of multi-scale oceanic processes in the SCS.
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funded by the Key-Area Research and Development Program of Guangdong Province(Grant No.2020B1111200001)the Key project of monitoring,early warning and prevention of major natural disasters of China(Grant No.2019YFC1510304)+1 种基金the S&T Program of Hebei(Grant No.19275408D)the Scientific Research Projects of Weather Modification in Northwest China(Grant No.RYSY201905).
文摘A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clouds in different developmental phases,two-dimensional(2D)and three-dimensional(3D)models are proposed by applying reflectivity factors at 0.5°and at 0.5°,1.5°,and 2.4°elevation angles,respectively.According to the thresholds of the algorithm,which include echo intensity,the echo top height of 35 dBZ(ET),density threshold,andεneighborhood,cloud clusters can be marked into four types:deep-convective cloud(DCC),shallow-convective cloud(SCC),hybrid convective-stratiform cloud(HCS),and stratiform cloud(SFC)types.Each cloud cluster type is further identified as a core area and boundary area,which can provide more abundant cloud structure information.The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing,Xuzhou,and Qingdao.The results show that cloud clusters can be intuitively identified as core and boundary points,which change in area continuously during the process of convective evolution,by the improved DBSCAN algorithm.Therefore,the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification.Because density thresholds are different and multiple elevations are utilized in the 3D model,the identified echo types and areas are dissimilar between the 2D and 3D models.The 3D model identifies larger convective and stratiform clouds than the 2D model.However,the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds.In addition,the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.
基金supported by the National Natural Science Foundation of China(6107207061301179)the National Science and Technology Major Project(2010ZX03006-002-04)
文摘To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） 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. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.
基金Projects(41161020,41261026) supported by the National Natural Science Foundation of ChinaProject(BQD2012013) supported by the Research starting Funds for Imported Talents,Ningxia University,China+1 种基金Project(ZR1209) supported by the Natural Science Funds,Ningxia University,ChinaProject(NGY2013005) supported by the Key Science Project of Colleges and Universities in Ningxia,China
文摘To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.
基金This work was supported in part by the National Natural Science Foundation of China under project contracts[NOS.61601093,61791082,61701116,61371047]in part by Sichuan Provincial Science and Technology Planning Program of China under project contracts No.2016GZ0061 and No.2018HH0044+2 种基金in part by Guangdong Provincial Science and Technology Planning Program of China under project contracts No.2015B090909004 and No.2016A010101036in part by the fundamental research funds for the Central Universities under project contract No.ZYGX2016Z011in part by Science and Technology on Electronic Information Control Laboratory.
文摘A priori knowledge of the number of tags is crucial for anti-collision protocols in slotted UHF RFID systems.The number of tags is used to decide optimal frame length in dynamic frame slotted ALOHA(DFSA)and to adjust access probability in random access protocols.Conventional researches estimate the number of tags in MAC layer based on statistics of empty slots,collided slots and successful slots.Usually,a collision detection algorithm is employed to determine types of time slots.Only three types are distinguished because of lack of ability to detect the number of tags in single time slot.In this paper,a physical layer algorithm is proposed to detect the number of tags in a collided slot.Mean shift algorithm is utilized,and some properties of backscatter signals are investigated.Simulation results verify the effectiveness of the proposed solution in terms of low estimation error with a high SNR range,outperforming the existing MAC layer approaches.
基金supported by the China Datang Corporation project“Study on the performance improvement scheme of in-service wind farms”,the Fundamental Research Funds for the Central Universities(2020MS021)the Foundation of State Key Laboratory“Real-time prediction of offshore wind power and load reduction control method”(LAPS2020-07).
文摘The simulation of wind power time series is a key process in renewable power allocation planning,operation mode calculation,and safety assessment.Traditional single-point modeling methods discretely generate wind power at each moment;however,they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency.To resolve this problem,a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed.First,a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented.Second,considering the typical daily output processes as status variables,a wind power time series simulation model based on Markov algorithm is constructed.Finally,a case is analyzed based on the measured data of a wind farm in China.The proposed model is then compared with traditional methods to verify its effectiveness and applicability.The comparison results indicate that the statistical characteristics,probability distributions,and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods.Moreover,modeling efficiency considerably improves.
文摘A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design.This paper proposes a Color Image Quick Fuzzy C-Means(CIQFCM)clustering algorithm based on clustering spatial mapping.First,the clustering sample space was mapped from the image pixels to the quantized color space,and several methods were adopted to compress the amount of clustering samples.Then,an improved pedigree clustering algorithm was applied to obtain the initial class centers.Finally,CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image.After theoretical analysis of the effect and efficiency of the CIQFCM algorithm,several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm.The results indicated that the value of quantization intervals should be set to 4,and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect.In addition,as the image size increased from 128×128 to 1024×1024,the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times,which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.
基金Project(61103046) supported in part by the National Natural Science Foundation of ChinaProject(B201312) supported by DHU Distinguished Young Professor Program,China+1 种基金Project(LY14F020007) supported by Zhejiang Provincial Natural Science Funds of ChinaProject(2014A610072) supported by the Natural Science Foundation of Ningbo City,China
文摘DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.
