We investigate the design of anonymous voting protocols,CV-based binary-valued ballot and CV-based multi-valued ballot with continuous variables（CV） in a multi-dimensional quantum cryptosystem to ensure the security...We investigate the design of anonymous voting protocols,CV-based binary-valued ballot and CV-based multi-valued ballot with continuous variables（CV） in a multi-dimensional quantum cryptosystem to ensure the security of voting procedure and data privacy.The quantum entangled states are employed in the continuous variable quantum system to carry the voting information and assist information transmission,which takes the advantage of the GHZ-like states in terms of improving the utilization of quantum states by decreasing the number of required quantum states.It provides a potential approach to achieve the efficient quantum anonymous voting with high transmission security,especially in large-scale votes.展开更多
Oscillatory failure cases(OFC)detection in the fly-by-wire(FBW)flight control system for civil aircraft is addressed in this paper.First,OFC is ranked four levels:Handling quality,static load,global structure fatigue ...Oscillatory failure cases(OFC)detection in the fly-by-wire(FBW)flight control system for civil aircraft is addressed in this paper.First,OFC is ranked four levels:Handling quality,static load,global structure fatigue and local fatigue,according to their respect impact on aircraft.Second,we present voting and comparing monitors based on un-similarity redundancy commands to detect OFC.Third,the associated performances,the thresholds and the counters of the monitors are calculated by the high fidelity nonlinear aircraft models.Finally,the monitors of OFC are verified by the Iron Bird Platform with real parameters of the flight control system.The results show that our approach can detect OFC rapidly.展开更多
Based on traveling ballot mode,we propose a secure quantum anonymous voting via Greenberger–Horne–Zeilinger(GHZ)states.In this scheme,each legal voter performs unitary operation on corresponding position of particle...Based on traveling ballot mode,we propose a secure quantum anonymous voting via Greenberger–Horne–Zeilinger(GHZ)states.In this scheme,each legal voter performs unitary operation on corresponding position of particle sequence to encode his/her voting content.The voters have multiple ballot items to choose rather than just binary options“yes”or“no”.After counting votes phase,any participant who is interested in voting results can obtain the voting results.To improve the efficiency of the traveling quantum anonymous voting scheme,an optimization method based on grouping strategy is also presented.Compared with the most existing traveling quantum voting schemes,the proposed scheme is more practical because of its privacy,verifiability and non-repeatability.Furthermore,the security analysis shows that the proposed traveling quantum anonymous voting scheme can prevent various attacks and ensure high security.展开更多
Purpose:The main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately.To serve this purpose,a novel approach is introduced to determine the likelihood of an ...Purpose:The main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately.To serve this purpose,a novel approach is introduced to determine the likelihood of an object to be extremely different from the general behavior of the entire dataset.Design/methodology/approach:This paper proposes a novel two-level approach based on the integration of bagging and voting techniques for anomaly detection problems.The proposed approach,named Bagged and Voted Local Outlier Detection(BV-LOF),benefits from the Local Outlier Factor(LOF)as the base algorithm and improves its detection rate by using ensemble methods.Findings:Several experiments have been performed on ten benchmark outlier detection datasets to demonstrate the effectiveness of the BV-LOF method.According to the results,the BV-LOF approach significantly outperformed LOF on 9 datasets of 10 ones on average.Research limitations:In the BV-LOF approach,the base algorithm is applied to each subset data multiple times with different neighborhood sizes(k)in each case and with different ensemble sizes(T).In our study,we have chosen k and T value ranges as[1-100];however,these ranges can be changed according to the dataset handled and to the problem addressed.Practical implications:The proposed method can be applied to the datasets from different domains(i.e.health,finance,manufacturing,etc.)without requiring any prior information.Since the BV-LOF method includes two-level ensemble operations,it may lead to more computational time than single-level ensemble methods;however,this drawback can be overcome by parallelization and by using a proper data structure such as R*-tree or KD-tree.Originality/value:The proposed approach(BV-LOF)investigates multiple neighborhood sizes(k),which provides findings of instances with different local densities,and in this way,it provides more likelihood of outlier detection that LOF may neglect.It also brings many benefits such as easy implementation,improved capability,higher applicability,and interpretability.展开更多
Average （mean） voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this pap...Average （mean） voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this paper,a new generation of average voter based on parallel algorithms and parallel random access machine（PRAM） structure are proposed.The analysis shows that this algorithm is optimal due to its improved time complexity,speed-up,and efficiency and is especially appropriate for applications where the size of input space is large.展开更多
Purpose:This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023.By integrating bibliometric analysis with expert insights from the Deep-time Digital Earth(DDE)initiative,t...Purpose:This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023.By integrating bibliometric analysis with expert insights from the Deep-time Digital Earth(DDE)initiative,this article identifies key emerging themes shaping the landscape of Earth Sciences①.Design/methodology/approach:The identification process involved a meticulous analysis of over 400,000 papers from 466 Geosciences journals and approximately 5,800 papers from 93 interdisciplinary journals sourced from the Web of Science and Dimensions database.To map relationships between articles,citation networks were constructed,and spectral clustering algorithms were then employed to identify groups of related research,resulting in 407 clusters.Relevant research terms were extracted using the Log-Likelihood Ratio(LLR)algorithm,followed by statistical analyses on the volume of papers,average publication year,and average citation count within each cluster.Additionally,expert knowledge from DDE Scientific Committee was utilized to select top 30 trends based on their representation,relevance,and impact within Geosciences,and finalize naming of these top trends with consideration of the content and implications of the associated research.This comprehensive approach in systematically delineating and characterizing the trends in a way which is understandable to geoscientists.Findings:Thirty significant trends were identified in the field of Geosciences,spanning five domains:deep space,deep time,deep Earth,habitable Earth,and big data.These topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society,science,and technology.Research limitations:The analyzed data of this study only contain those were included in the Web of Science.Practical implications:This study will strongly support the organizations and individual scientists to understand the modern frontier of earth science,especially on solid earth.The organizations such as the surveys or natural science fund could map out areas for future exploration and analyze the hot topics reference to this study.Originality/value:This paper integrates bibliometric analysis with expert insights to highlight the most significant trends on earth science and reach the individual scientist and public by global voting.展开更多
A method for terrain classification based on vibration response resulted from wheel-terrain interaction is presented. Four types of terrains including sine,gravel,cement and pebble were tested.The vibration data were ...A method for terrain classification based on vibration response resulted from wheel-terrain interaction is presented. Four types of terrains including sine,gravel,cement and pebble were tested.The vibration data were collected by two single axis accelerometers and a triaxial seat pad accelerometer,and five data sources were utilized. The feature vectors were obtained by combining features extracted from amplitude domain,frequency domain,and time-frequency domain. The ReliefF algorithm was used to evaluate the importance of attributes; accordingly,the optimal feature subsets were selected. Further,the predicted class was determined by fusion of outputs provided by five data sources. Finally,a voting algorithm,wherein a class with the most frequent occurrence is the predicted class,was employed. In addition,four different classifiers,namely support vector machine,k-nearest neighbors,Nave Bayes,and decision tree,were used to perform the classification and to test the proposed method. The results have shown that performances of all classifiers are improved.Therefore,the proposed method is proved to be effective.展开更多
为解决当前城市轨道交通(简称:城轨)列车客流分析存在的检测精度不高和适用场景单一等问题,设计了一种基于异质集成学习方法的城轨列车智能客流分析系统。该系统基于云边协同架构,采用分组Voting方法,将YOLOv5s(You Only Look Once v5s)...为解决当前城市轨道交通(简称:城轨)列车客流分析存在的检测精度不高和适用场景单一等问题,设计了一种基于异质集成学习方法的城轨列车智能客流分析系统。该系统基于云边协同架构,采用分组Voting方法,将YOLOv5s(You Only Look Once v5s)、FCHD(Fully Convolutional Head Detector)、CSRNet(Network for Congested Scene Recognition)模型作为基模型进行集成,最终实现客流统计、拥挤度分析和辅助清客等功能。利用北京城轨某线路列车的监控图像数据进行实验,结果表明,与其他各基模型相比,该系统采用的模型检测效果更佳,有效提升了检测精度,丰富了可适用的检测场景。展开更多
In order to search for reasonable air conditioned indoor control variables and save energy consumption and meet to need of personal thermal comfort, a method which is based on numerical simulation is employed to optim...In order to search for reasonable air conditioned indoor control variables and save energy consumption and meet to need of personal thermal comfort, a method which is based on numerical simulation is employed to optimize indoor control variables. Computational fluid dynamics （CFD） is used to describe thermal state of office. An optimal method is proposed in this paper, dual neural network model is firstly used to acquire reliable information, data from CFD model are pre pro cessed, and the remaining data are used to train artificial neural networks （ANN）, then CFD model is replaced by ANN model to reduce computational cost when is optimized, indoor control variables are optimized by genetic algorithm. Simulation results show that indoor thermal comfort is improved obviously, and the energy cost is decreased accordingly.展开更多
The '2011 Amazing China-The Most Attractive Chinese Cities for Foreigners,' an online poll for foreigners to choose their favorite cities,invites all interested foreigners to cast their votes.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61272495,61379153,and 61401519)the Research Fund for the Doctoral Program of Higher Education of China(Grant No.20130162110012)the MEST-NRF of Korea(Grant No.2012-002521)
文摘We investigate the design of anonymous voting protocols,CV-based binary-valued ballot and CV-based multi-valued ballot with continuous variables（CV） in a multi-dimensional quantum cryptosystem to ensure the security of voting procedure and data privacy.The quantum entangled states are employed in the continuous variable quantum system to carry the voting information and assist information transmission,which takes the advantage of the GHZ-like states in terms of improving the utilization of quantum states by decreasing the number of required quantum states.It provides a potential approach to achieve the efficient quantum anonymous voting with high transmission security,especially in large-scale votes.
文摘Oscillatory failure cases(OFC)detection in the fly-by-wire(FBW)flight control system for civil aircraft is addressed in this paper.First,OFC is ranked four levels:Handling quality,static load,global structure fatigue and local fatigue,according to their respect impact on aircraft.Second,we present voting and comparing monitors based on un-similarity redundancy commands to detect OFC.Third,the associated performances,the thresholds and the counters of the monitors are calculated by the high fidelity nonlinear aircraft models.Finally,the monitors of OFC are verified by the Iron Bird Platform with real parameters of the flight control system.The results show that our approach can detect OFC rapidly.
基金supported by the Tang Scholar Project of Soochow Universitythe National Natural Science Foundation of China(Grant No.61873162)+1 种基金the Fund from Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication NetworkSuzhou Key Laboratory of Advanced Optical Communication Network Technology。
文摘Based on traveling ballot mode,we propose a secure quantum anonymous voting via Greenberger–Horne–Zeilinger(GHZ)states.In this scheme,each legal voter performs unitary operation on corresponding position of particle sequence to encode his/her voting content.The voters have multiple ballot items to choose rather than just binary options“yes”or“no”.After counting votes phase,any participant who is interested in voting results can obtain the voting results.To improve the efficiency of the traveling quantum anonymous voting scheme,an optimization method based on grouping strategy is also presented.Compared with the most existing traveling quantum voting schemes,the proposed scheme is more practical because of its privacy,verifiability and non-repeatability.Furthermore,the security analysis shows that the proposed traveling quantum anonymous voting scheme can prevent various attacks and ensure high security.
文摘Purpose:The main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately.To serve this purpose,a novel approach is introduced to determine the likelihood of an object to be extremely different from the general behavior of the entire dataset.Design/methodology/approach:This paper proposes a novel two-level approach based on the integration of bagging and voting techniques for anomaly detection problems.The proposed approach,named Bagged and Voted Local Outlier Detection(BV-LOF),benefits from the Local Outlier Factor(LOF)as the base algorithm and improves its detection rate by using ensemble methods.Findings:Several experiments have been performed on ten benchmark outlier detection datasets to demonstrate the effectiveness of the BV-LOF method.According to the results,the BV-LOF approach significantly outperformed LOF on 9 datasets of 10 ones on average.Research limitations:In the BV-LOF approach,the base algorithm is applied to each subset data multiple times with different neighborhood sizes(k)in each case and with different ensemble sizes(T).In our study,we have chosen k and T value ranges as[1-100];however,these ranges can be changed according to the dataset handled and to the problem addressed.Practical implications:The proposed method can be applied to the datasets from different domains(i.e.health,finance,manufacturing,etc.)without requiring any prior information.Since the BV-LOF method includes two-level ensemble operations,it may lead to more computational time than single-level ensemble methods;however,this drawback can be overcome by parallelization and by using a proper data structure such as R*-tree or KD-tree.Originality/value:The proposed approach(BV-LOF)investigates multiple neighborhood sizes(k),which provides findings of instances with different local densities,and in this way,it provides more likelihood of outlier detection that LOF may neglect.It also brings many benefits such as easy implementation,improved capability,higher applicability,and interpretability.
文摘Average （mean） voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this paper,a new generation of average voter based on parallel algorithms and parallel random access machine（PRAM） structure are proposed.The analysis shows that this algorithm is optimal due to its improved time complexity,speed-up,and efficiency and is especially appropriate for applications where the size of input space is large.
文摘Purpose:This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023.By integrating bibliometric analysis with expert insights from the Deep-time Digital Earth(DDE)initiative,this article identifies key emerging themes shaping the landscape of Earth Sciences①.Design/methodology/approach:The identification process involved a meticulous analysis of over 400,000 papers from 466 Geosciences journals and approximately 5,800 papers from 93 interdisciplinary journals sourced from the Web of Science and Dimensions database.To map relationships between articles,citation networks were constructed,and spectral clustering algorithms were then employed to identify groups of related research,resulting in 407 clusters.Relevant research terms were extracted using the Log-Likelihood Ratio(LLR)algorithm,followed by statistical analyses on the volume of papers,average publication year,and average citation count within each cluster.Additionally,expert knowledge from DDE Scientific Committee was utilized to select top 30 trends based on their representation,relevance,and impact within Geosciences,and finalize naming of these top trends with consideration of the content and implications of the associated research.This comprehensive approach in systematically delineating and characterizing the trends in a way which is understandable to geoscientists.Findings:Thirty significant trends were identified in the field of Geosciences,spanning five domains:deep space,deep time,deep Earth,habitable Earth,and big data.These topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society,science,and technology.Research limitations:The analyzed data of this study only contain those were included in the Web of Science.Practical implications:This study will strongly support the organizations and individual scientists to understand the modern frontier of earth science,especially on solid earth.The organizations such as the surveys or natural science fund could map out areas for future exploration and analyze the hot topics reference to this study.Originality/value:This paper integrates bibliometric analysis with expert insights to highlight the most significant trends on earth science and reach the individual scientist and public by global voting.
基金Supported by the National Natural Science Foundation of China(51005018)
文摘A method for terrain classification based on vibration response resulted from wheel-terrain interaction is presented. Four types of terrains including sine,gravel,cement and pebble were tested.The vibration data were collected by two single axis accelerometers and a triaxial seat pad accelerometer,and five data sources were utilized. The feature vectors were obtained by combining features extracted from amplitude domain,frequency domain,and time-frequency domain. The ReliefF algorithm was used to evaluate the importance of attributes; accordingly,the optimal feature subsets were selected. Further,the predicted class was determined by fusion of outputs provided by five data sources. Finally,a voting algorithm,wherein a class with the most frequent occurrence is the predicted class,was employed. In addition,four different classifiers,namely support vector machine,k-nearest neighbors,Nave Bayes,and decision tree,were used to perform the classification and to test the proposed method. The results have shown that performances of all classifiers are improved.Therefore,the proposed method is proved to be effective.
文摘为解决当前城市轨道交通(简称:城轨)列车客流分析存在的检测精度不高和适用场景单一等问题,设计了一种基于异质集成学习方法的城轨列车智能客流分析系统。该系统基于云边协同架构,采用分组Voting方法,将YOLOv5s(You Only Look Once v5s)、FCHD(Fully Convolutional Head Detector)、CSRNet(Network for Congested Scene Recognition)模型作为基模型进行集成,最终实现客流统计、拥挤度分析和辅助清客等功能。利用北京城轨某线路列车的监控图像数据进行实验,结果表明,与其他各基模型相比,该系统采用的模型检测效果更佳,有效提升了检测精度,丰富了可适用的检测场景。
基金Supported by Basic Research Foundation of Beijing Institute of Technology(20070542009)
文摘In order to search for reasonable air conditioned indoor control variables and save energy consumption and meet to need of personal thermal comfort, a method which is based on numerical simulation is employed to optimize indoor control variables. Computational fluid dynamics （CFD） is used to describe thermal state of office. An optimal method is proposed in this paper, dual neural network model is firstly used to acquire reliable information, data from CFD model are pre pro cessed, and the remaining data are used to train artificial neural networks （ANN）, then CFD model is replaced by ANN model to reduce computational cost when is optimized, indoor control variables are optimized by genetic algorithm. Simulation results show that indoor thermal comfort is improved obviously, and the energy cost is decreased accordingly.
文摘The '2011 Amazing China-The Most Attractive Chinese Cities for Foreigners,' an online poll for foreigners to choose their favorite cities,invites all interested foreigners to cast their votes.
