Design of Compliant Straight-line Mechanisms Using Flexural Joints

Straight-line compliant mechanisms are important building blocks to design a linear-motion stage, which is very useful in precision applications. However, only a few configurations of straight-line compliant mechanisms are applicable. To construct more kinds of them, an approach to design large-displacement straight-line flexural mechanisms with rotational flexural joints is proposed, which is based on a viewpoint that the straight-line motion is regarded as a compromise of rigid and compliant parasitic motion of a rotational flexural joint. An analytical design method based on the Taylor series expansion is proposed to quickly obtain an approximate solution. To illustrate and verify the proposed method, two kinds of flexural joints, cross-axis hinge and leaf-type isosceles-trapezoidal flexural（LITF） pivot are used to reconstruct straight-line flexural mechanisms. Their performances are obtained by analytic and FEA method respectively. The comparisons of the results show the accuracy of the approach. Both examples show that the proposed approach can convert a large-deflection flexural joint into approximate straight-line mechanism with a high linearity that is higher than 5 000 within 5 man displacement. This can lead to a new way to design, analyze or optimize straight-line flexure mechanisms.

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。

Study on Numerical Comparison Method of Four-bar Straight-line Guidance Mechanism

In order to solve four-bar straight-line guidance mechanism synthesis problem for the arbitrarily given straight-line’s"angle requirement"and"point-position requirement",a numerical comparison synthesis method for single and double straight-line guidance mechanism is presented,which is convenient to realize by computer program.The basic idea of this method is:to select a four-bar linkage whose relative bar length of crank is 1 as a basic four-bar linkage.Then the other three relative bars’length is changed,and a lot of basic four-bar linkage can be obtained.There are many single and double ball-points of each basic four-bar linkage.With the motion of a basic four-bar linkage,there is straight-line segment of each Ball-point’s path.The data of these basic four-bar linkages is saved to a database.When designing a four-bar straight-line guidance mechanism,the design data is compared with the data in database and a satisfactory four-bar linkage can be obtained.The method effectively solves the straight-line guidance mechanism synthesis problem.

The Straight-Line Depreciation Method Used by Selected Companies and Educational Institutions in the Philippines

The straight-line method in computing for depreciation expense is the prevailing method used in the Philippines. This paper aims to determine the rationale behind the use of this method. The objective of the study is to determine the length of time within which the depreciation method is used, reasons in using the method, the rate of depreciation used by the companies, and the effects of the depreciation expense on their operating expenses. It also determines if the companies＇ decisions to use the straight-line method are influenced by the factors mentioned by Reynolds （196 I）----expected amount of services over the life of assets, the amount and timing of operating costs, the decline in the physical efficiency of the assets, and the rate of return--and if they considered capital investments and tax reduction in using this method. The study shows that companies and educational institutions use the straight-line method of computing depreciation expenses, because it is easy to use in computing the depreciation expenses, in comparing with previous years＇ computations, and in keeping track of the expenses. It is also convenient for tax administration and financial reporting. The rate of depreciation used varies, because the companies and educational institutions use their past experiences in determining the life of fixed assets. The percentage of depreciation to the operating expenses also varies. The companies and educational institutions adhered to the factors mentioned by Reynolds （1961） in choosing the straight-line method of depreciation. The companies did not consider reduction of tax in using the straight-line method.

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.

基于Tensor Voting的蚁蛉翅脉修补

针对蚁蛉模式识别中蚁蛉翅脉断裂问题,利用Tensor Voting技术修补其数字照片中断裂的翅脉;展示将其应用于蚁蛉模式识别前期处理,以获取主要翅脉尽量完整信息的算法;数值实验中采用3种蚁蛉翅的图像作为测试,收到了很好的结果.

应用Voting Machine构建研究型、互动型的双语物理课堂的研究与实践

高等教育"质量工程"的实施为高等学校本科教学提出了更新、更高的要求和挑战.本文报道了应用Voting Machine这一具有强大的互动和统计功能的教学设备在双语物理课堂上开展研究型、互动型教学的实践和研究成果.

BFV-Blockchainvoting:支持BFV全同态加密的区块链电子投票系统

当前的电子投票系统大多依赖于中心服务器和可信第三方,这种系统架构增加了投票的安全隐患,甚至使投票可能失败。为了解决这一问题,将区块链技术应用于电子投票系统,使区块链代替可信第三方,提出了一种支持BFV全同态加密的区块链电子投票系统BFV-Blockchainvoting。首先,用一个公开透明的公告板记录选票信息,同时设计了智能合约来实现验证、自计票功能;其次,为进一步提高投票过程的安全可靠性,使用SM2签名算法对投票者的注册信息进行签名处理,再选择能够互相监督的双方共同监管选票,并使用BFV同态加密算法来隐藏计票数据。经过测试与分析,所提系统单张选票的计票时间平均为1.69ms。所提方案可以为投票过程中的不可操纵性、匿名性、可验证性、不可重用性、不可胁迫性和抗量子攻击等安全属性提供保障,适用于多种投票场合,并且可以满足大型投票场景下的高效率需求。

基于MC3投票法和机器学习的信用风险评估

信用风险评估是金融风险管理的重要问题,为提高信用风险评估的有效性,基于马尔科夫链蒙特卡罗模型综合方法提出模型投票方法,这种方法可以不需要进行指标剔除,减少了特征选择过程中的信息丢失,同时可以更为谨慎地评估信用风险。结合MC3投票法和机器学习方法,构建了信用风险评估模型。在此基础上,借助国泰安数据库上市制造业企业的财务指标数据,对构建的信用风险评估模型与其他模型的预测性能进行了比较分析。检验结果表明:相对于一次剔除法和逐步剔除法,MC3投票法降低了银行由于信用风险评估模型的一类错误而造成的损失,从而提高了信用风险评估模型的性能。

多特征融合的Voting-SRM情感分类研究

情感分类是自然语言处理领域的一个核心问题,其目的是判断评论文本的情感极性,并挖掘其蕴含的情感价值信息.为了提取评论文本中潜在的情感信息,提高分类精度,本文提出了多特征融合的Voting-SRM情感分类方法.结合词性特征,语法特征等,提取名词,动词,形容词,副词等特征,然后运用软投票机制,结合随机梯度下降算法、随机森林、神经网络等算法,对已获取评论文本进行极性二分类.本文通过对比实验,验证了该方法的有效性.

物理课堂教学评价的一种先进工具———Voting Machine评价系统介绍

本文介绍美国俄亥俄州立大学研制的Voting Machine教学评价系统,详细论述了该教学评价系统的组成和安装、师生使用方法以及系统的理论依据。

Dynamic weighted voting for multiple classifier fusion:a generalized rough set method

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）.

Multi-Candidate Voting Model Based on Blockchain

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.

Anonymous voting for multi-dimensional CV quantum system

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.

A Secure Anonymous Internet Electronic Voting Scheme Based on the Polynomial

In this paper, we use the polynomial function and Chaum＇s RSA （Rivest, Shamir, Adleman） blind signature scheme to construct a secure anonymous internet electronic voting scheme. In our scheme, each vote does not need to be revealed in the tallying phase. The ballot number of each candidate gets is counted by computing the degrees of two polynomials＇ greatest common divisor. Our scheme does not require a special voting channel and communication can occur entirely over the current internet.

Constrained voting extreme learning machine and its application

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.

A random forest algorithm based on similarity measure and dynamic weighted voting

The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model.

Improved PBFT protocol based on phase voting and threshold signature

The communication complexity of the practical byzantine fault tolerance(PBFT)protocol is reduced with the threshold signature technique applied to the consensus process by phase voting PBFT(PV-PBFT).As most communication occurs between the primary node and replica nodes in PV-PVFT,consistency verification is accomplished through threshold signatures,multi-PV,and multiple consensus.The view replacement protocol introduces node weights to influence the election of a primary node,reducing the probability of the same node being elected primary multiple times.The experimental results of consensus algorithms show that compared to PBFT,the communication overhead of PV-PBFT decreases by approximately 90% with nearly one-time improvement in the throughput relative and approximately 2/3 consensus latency,lower than that of the scalable hierarchical byzantine fault tolerance.The communication complexity of the PBFT is O(N^(2)),whereas that of PV-PBFT is only O(N),which implies the significant improvement of the operational efficiency of the blockchain system.

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%).

Oscillatory Failure Detection for Flight Control System Using Voting and Comparing Monitors

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.