基于voting集成的智能电能表故障多分类方法

为提升智能电能表故障准确分类能力,助力维护人员迅速排除故障,提出基于投票法voting集成的智能电能表故障多分类方法。针对实际智能电能表故障数据进行编码预处理,基于皮尔逊系数法筛选智能电能表故障分类关键影响因素,结合合成少数类过采样技术(synthetic minority oversampling technique, SMOTE)算法解决数据类别不平衡问题,由此建立模型所需数据集,再通过投票法进行模型融合,结合粒子群PSO(particle swarm optimization)确定各基模型的权重,据此构建基于极限梯度提升树(extreme gradient boosting trees, XGBT)、K近邻(k-nearest neighbor, KNN)和朴素贝叶斯(naive bayes, NB)模型的智能电能表故障多分类方法。实测实验结果表明:所提出方法能有效实现智能电能表的故障快速准确分类,与现有方法相比,在智能电能表的故障分类精确率、召回率及F1-Score均有明显提升。

An Adaptive DDoS Detection and Classification Method in Blockchain Using an Integrated Multi-Models

With the rising adoption of blockchain technology due to its decentralized,secure,and transparent features,ensuring its resilience against network threats,especially Distributed Denial of Service(DDoS)attacks,is crucial.This research addresses the vulnerability of blockchain systems to DDoS assaults,which undermine their core decentralized characteristics,posing threats to their security and reliability.We have devised a novel adaptive integration technique for the detection and identification of varied DDoS attacks.To ensure the robustness and validity of our approach,a dataset amalgamating multiple DDoS attacks was derived from the CIC-DDoS2019 dataset.Using this,our methodology was applied to detect DDoS threats and further classify them into seven unique attack subcategories.To cope with the broad spectrum of DDoS attack variations,a holistic framework has been pro-posed that seamlessly integrates five machine learning models:Gate Recurrent Unit(GRU),Convolutional Neural Networks(CNN),Long-Short Term Memory(LSTM),Deep Neural Networks(DNN),and Support Vector Machine(SVM).The innovative aspect of our framework is the introduction of a dynamic weight adjustment mechanism,enhancing the system’s adaptability.Experimental results substantiate the superiority of our ensemble method in comparison to singular models across various evaluation metrics.The framework displayed remarkable accuracy,with rates reaching 99.71%for detection and 87.62%for classification tasks.By developing a comprehensive and adaptive methodology,this study paves the way for strengthening the defense mechanisms of blockchain systems against DDoS attacks.The ensemble approach,combined with the dynamic weight adjustment,offers promise in ensuring blockchain’s enduring security and trustworthiness.

Identification of cell surface markers for acute myeloid leukemia prognosis based on multi-model analysis

Given the extremely high inter-patient heterogeneity of acute myeloid leukemia(AML),the identification of biomarkers for prognostic assessment and therapeutic guidance is critical.Cell surface markers(CSMs)have been shown to play an important role in AML leukemogenesis and progression.In the current study,we evaluated the prognostic potential of all human CSMs in 130 AML patients from The Cancer Genome Atlas(TCGA)based on differential gene expression analysis and univariable Cox proportional hazards regression analysis.By using multi-model analysis,including Adaptive LASSO regression,LASSO regression,and Elastic Net,we constructed a 9-CSMs prognostic model for risk stratification of the AML patients.The predictive value of the 9-CSMs risk score was further validated at the transcriptome and proteome levels.Multivariable Cox regression analysis showed that the risk score was an independent prognostic factor for the AML patients.The AML patients with high 9-CSMs risk scores had a shorter overall and event-free survival time than those with low scores.Notably,single-cell RNA-sequencing analysis indicated that patients with high 9-CSMs risk scores exhibited chemotherapy resistance.Furthermore,PI3K inhibitors were identified as potential treatments for these high-risk patients.In conclusion,we constructed a 9-CSMs prognostic model that served as an independent prognostic factor for the survival of AML patients and held the potential for guiding drug therapy.

Density Clustering Algorithm Based on KD-Tree and Voting Rules

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.

A Bayesian multi-model inference methodology for imprecise momentindependent global sensitivity analysis of rock structures

Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.

Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure

Advanced Metering Infrastructure(AMI)is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control center.The massive amount of data collected supports the real-time decision-making required for diverse applications.The communication infrastructure relies on different network types,including the Internet.This makes the infrastructure vulnerable to various attacks,which could compromise security or have devastating effects.However,traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks.The objective of this paper is to develop an Anomaly Detection System(ADS)based on deep learning using the CIC-IDS2017 dataset.However,this dataset is highly imbalanced;thus,a two-step sampling technique:random under-sampling and the Synthetic Minority Oversampling Technique(SMOTE),is proposed to balance the dataset.The proposed system utilizes a multiple hidden layer Auto-encoder(AE)for feature extraction and dimensional reduction.In addition,an ensemble voting based on both Random Forest(RF)and Convolu-tional Neural Network(CNN)is developed to classify the multiclass attack cate-gories.The proposed system is evaluated and compared with six different state-of-the-art machine learning and deep learning algorithms:Random Forest(RF),Light Gradient Boosting Machine(LightGBM),eXtreme Gradient Boosting(XGboost),Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and bidirectional LSTM(biLSTM).Experimental results show that the proposed model enhances the detection for each attack class compared with the other machine learning and deep learning models with overall accuracy(98.29%),precision(99%),recall(98%),F_(1) score(98%),and the UNDetection rate(UND)(8%).

An Innovative Method to Improve Model Accuracy by Implementing Multi-models Scheme for 28nm Node and Below

As the process comes into 28nm node and below,lithography struggles stronger between high resolution (high NA) and enough process window especially for hole layers (Contacts and Vias).Taking more care of process window may result in lower image quality of structures and bigger uncertainty in OPC model accuracy.Besides,it is difficult to cover all kinds of test structures within acceptable accuracy in one OPC model because of distinct difference of image quality of different patterns.To solve these problems,this paper introduces an innovative method of applying multi-models in one layer OPC.According to different characteristic features,multiple models are applied respectively and the fitting on these features with poor resolution can be improved by re-optimizing based on related model.A practice for 28 nm Via layer modeling calibration is given,and it shows an evident improvement of model accuracy through the implementing of multiple models scheme.

基于Voting机制的IMA-BP不平衡数据分类算法

针对传统分类模型在实际应用中对提取到的不平衡数据特征进行分类时分类结果精度低的问题,提出使用蜉蝣算法(mayfly algorithm,MA)优化的反向传播(back propogation,BP)神经网络分类模型。同时为了提升算法前期全局搜索能力和后期局部搜索能力,引入阻尼比系数和非线性惯性权重因子,构建出改进蜉蝣算法(improved mayfly algorithm,IMA)优化的BP神经网络(IMA-BP)分类器。根据该分类器分类具有随机的特点,引入集成学习中的投票(Voting)机制,将IMA-BP作为弱分类器,将各弱分类器的分类结果通过软投票方法融合,构成了一个Voting机制的IMA-BP分类模型。为验证分类模型的性能,使用UCI数据库中的数据集将该模型与其他的模型进行比较,结果表明Voting机制的IMA-BP分类模型对4个数据集的分类准确率分别为88.67%、96.67%、91.25%、93.52%,都要高于其他模型,说明该分类模型具有较好准确性和可行性,对一些分类任务具有较强的指导作用和应用价值。

基于Voting和Stacking集成算法的岩爆倾向性预测

岩爆是矿山深部开采中常见的地质灾害,准确预测岩爆可降低矿山生产安全风险。将机器学习应用于岩爆预测是切实可行的,但仅用一种方法或将几种方法简单结合对于提高岩爆预测的准确性或泛化性作用十分有限。基于此,将最大切向应力、应力集中系数、脆性系数、弹性能量指数等作为指标,利用231组有效岩爆数据,基于Voting和Stacking集成算法,融合精确率较高的6种基础分类器(LR、RF、SVM、DT、KNN、GNB),建立了4个集成分类器V 1、V 2、S 1和S 2。根据预测结果的混淆矩阵计算了精确率、准确率、召回率及F 1分数,对各分类器性能进行了评估,结果表明:基础分类器中SVM、RF对Ⅰ级、Ⅱ级样本较敏感,KNN对Ⅲ级、Ⅳ级样本更敏感;RF、SVM整体预测效果最好,精确率分别为0.93、0.94;集成分类器相对于基础分类器性能均有不同程度的提升,但受性能较差的基础分类器及投票机制影响,Voting集成分类器整体性能弱于Stacking集成分类器;4个集成分类器中S 1性能提升最显著,预测效果最佳,精确率、准确率、召回率、F 1分数分别为0.95、0.97、0.96、0.95;将基于Stacking算法构建的集成分类器S 1应用于秦岭隧道的岩爆预测,预测结果与工程现场实际一致,验证了其可靠性。

Novel traveling quantum anonymous voting scheme via GHZ states

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.

Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human life.Open attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote access.These attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are subpar.This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization.The employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA optimizer.The proposed voting classifier categorizes the network intrusions robustly and efficiently.To assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization.The dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and significance.The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

Data Augmentation and Random Multi-Model Deep Learning for Data Classification

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML models.The increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen examples.Deep learning(DL)models have a lot of parameters,and they frequently overfit.Effectively,to avoid overfitting,data plays a major role to augment the latest improvements in DL.Nevertheless,reliable data collection is a major limiting factor.Frequently,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in batches.In this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for classification.We present a methodology for using Generative Adversarial Networks(GANs)to generate images for data augmenting.Through experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency.Experimenting across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

水利工程质量监督全过程全方位定量评价模型构建

为促进我国水利工程质量监督管理工作运行的高效化,确保工程监督的有效性和权威性,基于AHP(analytic hierarchy process)原理,针对水利部督查小组的现场检查情况,依据水利部下达的相关文件,结合负分制和一票否决制,将定性问题定量化,构建一套涉及水利工程建设、运行、管理全过程全方位的质量监督管理评价模型,共包含7层2112个指标。在综合试错的基础上,计算得出不同层级和不同指标的权重,然后选用该模型分析评价2020年7个省份受督查水利工程项目的质量管理与安全生产管理监督数据,间接说明模型的实用性和易操作性。评价结果表明:7个省份均在合格以上,整体质量管理情况较好,但得分差异显著,其中最高得分90.6、最低得分65.6,相差25分;不同类型管理评价结果显示,安全生产管理得分普遍高于质量管理得分;各参建单位评价结果显示,建设单位和勘察设计单位的情况最优、评分基本达到满分,而质量检测单位和施工单位得分最低、需重点加强相关监管。评价结果为水利工程监督管理工作提供了科学的数据支撑,构建的评价模型也为水利工程质量监督定量化提供了易实操的普适性工具。

越南2023年回顾与2024年展望

政治方面,2023年越南政治上妥善应对高层政局变动,选举产生新一任国家主席和两位副总理。对党、政、国会各级官员开展信任测评工作,以总结过去的工作成效、为下一步官员选任工作提供依据。成立五个工作小组、启动越共十四大筹备工作。经济方面,着力克服工业领域增速严重放缓、服务业增速未达预期、国际需求疲软导致进出口双下降等困难,有效维护宏观经济稳定、吸引外资额创历史新高,全年经济增速为5.05%,未能达成国会提出的增长目标,也成为2011年后经济增速第三低的年度。外交方面,越南走出大量外交官受纪律处分的阴霾,分别与中国建成具有战略意义的命运共同体,与美国、日本建成全面战略伙伴关系,取得诸多“具有历史性的成就”。展望2024年,越南将启动党政换届工作,总结施政经验、讨论未来发展方向。外交上继续深化伙伴关系,谋求在多边组织中发挥更大作用。在出口和消费均有望增长的情况下,2024年越南经济增速有望提升,但外资流入可能放缓,越南基础设施建设薄弱等固有桎梏仍待克服。

基于香农熵代表性特征和投票机制的三维模型分类

目前基于视图的3维模型分类方法存在单视图视觉信息不充分、多视图信息冗余的问题,且同等对待所有视图会忽略不同投影视角之间的差异性。针对上述问题,该文提出一种基于香农熵代表性特征和投票机制的3维模型分类方法。首先,通过在3维模型周围均匀设置多个视角组来获取表征模型的多组视图集。为了有效提取视图深层特征,在特征提取网络中引入通道注意力机制;然后,针对Softmax函数输出的视图判别性特征,使用香农熵来选择代表性特征,从而避免多视图特征冗余;最后,基于多个视角组的代表性特征利用投票机制来完成3维模型分类。实验表明:该方法在3维模型数据集ModelNet10上的分类准确率达到96.48%,分类性能突出。

乡村振兴背景下绿色金融发展路径研究——以吉林省为例

绿色转型、乡村振兴作为党和国家提出重大发展战略,两者有很多契合点,为深入分析绿色金融对乡村振兴的支持作用,进一步找准绿色金融支持乡村振兴的着力点,本文梳理了吉林省绿色金融支持乡村振兴的优秀做法,对2018-2022年来绿色金融支持乡村振兴成效进行了实证分析,验证了绿色金融对乡村振兴的正向促进作用,并根据国内外绿色金融支持乡村振兴发展经验提出了相应政策建议。

物有所值视角下工程招投标评定分离定标办法

直接抽签定标法、票决定标法及票决低价定标法等定标办法均没有综合考虑定标候选人报价和综合得分,择优竞价性相对较差.基于物有所值(VFM)理论创新性地提出了票决指数法。对我国评定分离办法试点情况进行梳理,基于VFM理论构建票决指数法的初步模型和最合适区域模型,对比分析票决指数法与其他票决办法。结果表明:票决指数法是一种科学有效的评定分离定标办法,可为招标人更科学择优竞价定标提供一定的理论指导。

高校专业技术职务评聘投票系统的建设与应用——以天津医科大学为例

为进一步深化职称制度改革,天津医科大学在总结传统专业技术职务评聘过程中存在的问题的基础上,结合工作实际,设计了集材料申报、材料展示和专家评审一体化的专业技术评聘投票系统,并投入使用.本文对系统建设特点做了具体的介绍,并通过实际应用,实现了专业技术职务评聘信息化管理模式,极大提升了评审工作的效率和科学化水平.

股东会电子化中股东平等原则的规范构造

出席股东会是股东行使自身权利、治理公司事务的基本路径。委托代理人出席或书面表决等方式系原《公司法》认可的股东现场出席股东会的合法替代方式,但这两种方式均存在不足。鉴于实践效果上电子化表决在提升参与度和确保表决真实性方面起到了积极作用,新《公司法》明确规定公司可以通过电子通信方式召开电子股东会,进一步提高了股东参与度并克服了传统的两类替代方式的弊端。该规定是对实践中已广泛采用的股东会组织方式合法性的确认。然而,新《公司法》等现行法律法规并未充分考虑到电子股东会制度可能引发的股东知情权行权差异和潜在的股东不平等问题,也并未对上述问题以及电子股东会可能发生的程序瑕疵、系统障碍作出针对性的预防和救济规制。实施电子股东会的公司应当保障不同参与方式的股东均可以充分行使权利,制定电子股东会决策程序瑕疵、出现系统故障时的预防和救济措施,以全面有效发挥股东会应有的功能。