目的以中药药性作为特征变量,构建基于Voting集成算法的中药抗炎作用预测模型,并通过可视化技术分析不同药性特征对于中药抗炎作用的影响。方法以《中药学》与SymMap数据库中1247味中药为研究对象,经过初筛和复筛后建立包含性味归经等...目的以中药药性作为特征变量,构建基于Voting集成算法的中药抗炎作用预测模型,并通过可视化技术分析不同药性特征对于中药抗炎作用的影响。方法以《中药学》与SymMap数据库中1247味中药为研究对象,经过初筛和复筛后建立包含性味归经等特征的规范化数据库。基于决策树、支持向量机、轻量级梯度提升机等6种基础模型构建Voting集成模型,并以七折交叉验证和基于树结构的贝叶斯优化算法超参数优化提升模型性能。利用SHAP(SHapley Additive ex Planations)解释器可视化关键药性特征。结果经筛选后,共纳入522味抗炎中药构建数据库。Voting集成模型综合性能最优,F1分数为0.797,AUC值为0.77,较单一模型平均提升7.4%。SHAP分析表明使中药发挥抗炎作用的重要特征分别是“脾经”“甘味”“补益”等,使中药不具有抗炎作用的重要特征为“性温或平”和“毒性”。结论首次通过集成算法构建具有良好性能的中药抗炎作用预测模型,为中医药与机器学习结合的研究模式提供了新思路。展开更多
Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine lea...Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.展开更多
Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptibl...Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.展开更多
Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the ident...Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information.展开更多
Blockchain-based user-centric access network(UCAN)fails in dynamic access point(AP)management,as it lacks an incentive mechanism to promote virtuous behavior.Furthermore,the low throughput of the blockchain has been a...Blockchain-based user-centric access network(UCAN)fails in dynamic access point(AP)management,as it lacks an incentive mechanism to promote virtuous behavior.Furthermore,the low throughput of the blockchain has been a bottleneck to the widespread adoption of UCAN in 6G.In this paper,we propose Overlap Shard,a blockchain framework based on a novel reputation voting(RV)scheme,to dynamically manage the APs in UCAN.AP nodes in UCAN are distributed across multiple shards based on the RV scheme.That is,nodes with good reputation(virtuous behavior)are likely to be selected in the overlap shard.The RV mechanism ensures the security of UCAN because most APs adopt virtuous behaviors.Furthermore,to improve the efficiency of the Overlap Shard,we reduce cross-shard transactions by introducing core nodes.Specifically,a few nodes are overlapped in different shards,which can directly process the transactions in two shards instead of crossshard transactions.This greatly increases the speed of transactions between shards and thus the throughput of the overlap shard.The experiments show that the throughput of the overlap shard is about 2.5 times that of the non-sharded blockchain.展开更多
According to the Charter of the United Nations,the United Nations Security Council adopts a“collective security system”authorized voting system,which has prominent drawbacks such as difficulty in fully reflecting th...According to the Charter of the United Nations,the United Nations Security Council adopts a“collective security system”authorized voting system,which has prominent drawbacks such as difficulty in fully reflecting the will of all Member States.Combining interdisciplinary,qualitative and quantitative research methods,in response to the dilemma of Security Council voting reform,this article suggests retaining the Security Council voting system and recommending a simplified model of“basic and weighted half”for voting allocation.This model not only inherits the authorized voting system of the collective security system,but also follows the allocation system of sovereignty equality in the Charter.It can also achieve the“draw on the advantages and avoid disadvantages”of Member States towards international development,promote the transformation of“absolute equality”of overall consistency into“real fairness”relative to individual contributions,and further promote the development of international law in the United Nations voting system.展开更多
Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional...Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.展开更多
Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as i...Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.展开更多
This paper analyzes the price difference between superior voting （SV） and inferior voting （IV） shares for three dual-class firms： Farmer Mac as a big price discount case, Fox as a price similarity case, and Heico...This paper analyzes the price difference between superior voting （SV） and inferior voting （IV） shares for three dual-class firms： Farmer Mac as a big price discount case, Fox as a price similarity case, and Heico as a big price premium case. We show that the price difference is mainly affected by the control benefit, while voting power and liquidity are also relevant factors. We suggest that the control benefit can be revealed by examining share accumulation and firm performance.展开更多
Phishing is one of the most common social engineering attacks that users over the internet fall for. An example is voting systems, and because such systems should be accurate and error free, phishing prevention techni...Phishing is one of the most common social engineering attacks that users over the internet fall for. An example is voting systems, and because such systems should be accurate and error free, phishing prevention techniques are crucial. Visual Cryptography (VC) is utilized for efficient voting system authentication to cast votes. VC is one of the most secure approaches for privacy protection as it ensures the confidentiality of the voting system. This paper discusses proposed phishing prevention methods and compares different proposed methods.展开更多
In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatia...In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatial Refined Voting Mechanism(SRVM) is designed for standard Particle Swarm Optimization(PSO) to overcome the defects of local optimal and slow convergence.For each generation candidate particle positions are recorded and an adaptive cube is formed with own adaptive side length to indicate occupied regions. Then space voting begins and is sorted based on voting results, whose centers with bigger voting counts are seen as sub-optimal positions. The average of all particles of corresponding dimensions are calculated as the refined solutions. A time coordination method is developed by generating specified candidate paths for every UAV, making them arrive the same destination with the same time consumption. A spatial-temporal collision avoidance technique is introduced to make collision free. Distance to destination is constructed to improve the searching accuracy and velocity of particles. In addition, the objective function is redesigned by considering the obstacle and threat avoidance, Estimated Time of Arrival(ETA), separation maintenance and UAV self-constraints. Experimental results prove the effectiveness and efficiency of the algorithm.展开更多
To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to ...To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.展开更多
Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.Wit...Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.展开更多
文摘目的以中药药性作为特征变量,构建基于Voting集成算法的中药抗炎作用预测模型,并通过可视化技术分析不同药性特征对于中药抗炎作用的影响。方法以《中药学》与SymMap数据库中1247味中药为研究对象,经过初筛和复筛后建立包含性味归经等特征的规范化数据库。基于决策树、支持向量机、轻量级梯度提升机等6种基础模型构建Voting集成模型,并以七折交叉验证和基于树结构的贝叶斯优化算法超参数优化提升模型性能。利用SHAP(SHapley Additive ex Planations)解释器可视化关键药性特征。结果经筛选后,共纳入522味抗炎中药构建数据库。Voting集成模型综合性能最优,F1分数为0.797,AUC值为0.77,较单一模型平均提升7.4%。SHAP分析表明使中药发挥抗炎作用的重要特征分别是“脾经”“甘味”“补益”等,使中药不具有抗炎作用的重要特征为“性温或平”和“毒性”。结论首次通过集成算法构建具有良好性能的中药抗炎作用预测模型,为中医药与机器学习结合的研究模式提供了新思路。
文摘Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.
文摘Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.
文摘Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant 61931005.
文摘Blockchain-based user-centric access network(UCAN)fails in dynamic access point(AP)management,as it lacks an incentive mechanism to promote virtuous behavior.Furthermore,the low throughput of the blockchain has been a bottleneck to the widespread adoption of UCAN in 6G.In this paper,we propose Overlap Shard,a blockchain framework based on a novel reputation voting(RV)scheme,to dynamically manage the APs in UCAN.AP nodes in UCAN are distributed across multiple shards based on the RV scheme.That is,nodes with good reputation(virtuous behavior)are likely to be selected in the overlap shard.The RV mechanism ensures the security of UCAN because most APs adopt virtuous behaviors.Furthermore,to improve the efficiency of the Overlap Shard,we reduce cross-shard transactions by introducing core nodes.Specifically,a few nodes are overlapped in different shards,which can directly process the transactions in two shards instead of crossshard transactions.This greatly increases the speed of transactions between shards and thus the throughput of the overlap shard.The experiments show that the throughput of the overlap shard is about 2.5 times that of the non-sharded blockchain.
文摘According to the Charter of the United Nations,the United Nations Security Council adopts a“collective security system”authorized voting system,which has prominent drawbacks such as difficulty in fully reflecting the will of all Member States.Combining interdisciplinary,qualitative and quantitative research methods,in response to the dilemma of Security Council voting reform,this article suggests retaining the Security Council voting system and recommending a simplified model of“basic and weighted half”for voting allocation.This model not only inherits the authorized voting system of the collective security system,but also follows the allocation system of sovereignty equality in the Charter.It can also achieve the“draw on the advantages and avoid disadvantages”of Member States towards international development,promote the transformation of“absolute equality”of overall consistency into“real fairness”relative to individual contributions,and further promote the development of international law in the United Nations voting system.
基金National Natural Science Foundation of China Nos.61962054 and 62372353.
文摘Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.
基金This work was supported in part by Shandong Provincial Natural Science Foundation(ZR2019PF007)the National Key Research and Development Plan of China(2018YFB0803504)+2 种基金Basic Scientific Research Operating Expenses of Shandong University(2018ZQXM004)Guangdong Province Key Research and Development Plan(2019B010137004)the National Natural Science Foundation of China(U20B2046).
文摘Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.
文摘This paper analyzes the price difference between superior voting （SV） and inferior voting （IV） shares for three dual-class firms： Farmer Mac as a big price discount case, Fox as a price similarity case, and Heico as a big price premium case. We show that the price difference is mainly affected by the control benefit, while voting power and liquidity are also relevant factors. We suggest that the control benefit can be revealed by examining share accumulation and firm performance.
文摘Phishing is one of the most common social engineering attacks that users over the internet fall for. An example is voting systems, and because such systems should be accurate and error free, phishing prevention techniques are crucial. Visual Cryptography (VC) is utilized for efficient voting system authentication to cast votes. VC is one of the most secure approaches for privacy protection as it ensures the confidentiality of the voting system. This paper discusses proposed phishing prevention methods and compares different proposed methods.
基金co-supported by China Scholarship Council (No. 201604000003)the National Natural Science Foundation of China (Nos. U1433203, U1533119 and L142200032)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 61221061)
文摘In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatial Refined Voting Mechanism(SRVM) is designed for standard Particle Swarm Optimization(PSO) to overcome the defects of local optimal and slow convergence.For each generation candidate particle positions are recorded and an adaptive cube is formed with own adaptive side length to indicate occupied regions. Then space voting begins and is sorted based on voting results, whose centers with bigger voting counts are seen as sub-optimal positions. The average of all particles of corresponding dimensions are calculated as the refined solutions. A time coordination method is developed by generating specified candidate paths for every UAV, making them arrive the same destination with the same time consumption. A spatial-temporal collision avoidance technique is introduced to make collision free. Distance to destination is constructed to improve the searching accuracy and velocity of particles. In addition, the objective function is redesigned by considering the obstacle and threat avoidance, Estimated Time of Arrival(ETA), separation maintenance and UAV self-constraints. Experimental results prove the effectiveness and efficiency of the algorithm.
基金This project was supported by the National Basic Research Programof China (2001CB309403)
文摘To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.
基金supported by the National Natural Science Foundation of China(6177340561751312)the Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY020123)。
文摘Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.
