异构并行计算下高维混合型数据聚类算法研究

高维数据维度增加,数据空间的体积呈指数增长,容易陷入“维数灾难”,导致聚类算法执行效率低,为此,提出异构并行计算下高维混合型数据聚类算法。构建高维混合型数据相异度矩阵,提取高维混合型数据的统计序列特征值,利用时间窗口进行特征优化。采用K⁃Prototypes聚类算法提取高维混合型数据的统计序列特征,评估数据与类中心的相异性,计算数据与类中心的欧氏距离,实现高维混合型数据聚类。采用异构并行计算技术进行高维混合型数据K⁃Prototypes聚类的并行化处理,合理分配CPU与GPU工作,达到CPU与GPU的工作负载平衡,提高K⁃Prototypes的聚类效率。实验结果表明,此算法对于高维混合型数据的聚类效果好、运行时间短、性能稳定。

Theoretical and experimental investigations on an X-shaped vibration isolator with active controlled variable stiffness

A novel X-shaped variable stiffness vibration isolator(X-VSVI)is proposed.The Runge-Kutta method,harmonic balance method,and wavelet transform spectra are introduced to evaluate the performance of the X-VSVI under various excitations.The layer number,the installation angle of the X-shaped structure,the stiffness,and the active control parameters are systematically analyzed.In addition,a prototype of the X-VSVI is manufactured,and vibration tests are carried out.The results show that the proposed X-VSVI has a superior adaptability to that of a traditional X-shaped mechanism,and shows excellent vibration isolation performance in response to different amplitudes and forms of excitations.Moreover,the vibration isolation efficiency of the device can be improved by appropriate adjustment of parameters.

An Integrated Control Framework for Torque Vectoring and Active Suspension System

Four-wheel independently driven electric vehicles(FWID-EV)endow a flexible and scalable control framework to improve vehicle performance.This paper integrates the torque vectoring and active suspension system(ASS)to enhance the vehicle’s longitudinal and vertical motion control performance.While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden.To facilitate the controller design and ease the load,a half-vehicle dynamics system is built and simplified to the linear-time-varying(LTV)model.Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety,energy-saving and comfort requirements.The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption.Finally,the effectiveness of the proposed controller is verified through the rapid-control-prototype(RCP)test.The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.

Winding Function Model-based Performance Evaluation of a PM Transverse Flux Generator for Applications in Direct-drive Systems

The magnetic flux in a permanent magnet transverse flux generator(PMTFG) is three-dimensional(3D), therefore, its efficacy is evaluated using 3D magnetic field analysis. Although the 3D finite-element method(FEM) is highly accurate and reliable for machine simulation, it requires a long computation time, which is crucial when it is to be used in an iterative optimization process. Therefore, an alternative to 3DFEM is required as a rapid and accurate analytical technique. This paper presents an analytical model for PMTFG analysis using winding function method. To obtain the air gap MMF distribution, the excitation magneto-motive force(MMF) and the turn function are determined based on certain assumptions. The magnetizing inductance, flux density, and back-electro-magnetomotive force of the winding are then determined. To assess the accuracy of the proposed method, the analytically calculated parameters of the generator are compared to those obtained by a 3D-FEM. The presented method requires significantly shorter computation time than the 3D-FEM with comparable accuracy.

Few-shot image recognition based on multi-scale features prototypical network

In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric space.To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction.Additionally,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment.The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space.Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011.The method in this paper can achieve higher classification accuracy.Specifically,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two datasets.Moreover,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

The Advertisement Translation Based on the Prototype Theory

Based on the Prototype Theory,the prototypical feature of advertisement is found to be the combination of three language functions:the informative function,the expressive function,and the vocative function.The advertisement translation means the adjustment of the informative function and the expressive function according to the differences between languages or cultures in order to maximize the vocative function.The faithful translation is the closest to the prototype of the source text but not necessarily the best translation.

Rapid Prototype Development Approach for Genetic Programming

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

The Role and Place of Artificial Neural Network Architectures Structural Redundancy in the Input Data Prototypes and Generalization Development

Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.

Field trial measurement and channel modeling for reconfigurable intelligent surface

With the advantage of programmable electromagnetic properties,Reconfigurable Intelligent Surfaces(RISs)havedrawn wide attention from both industry and academia.RIS-assisted communication systems can promote hugewireless channel quality improvement and remarkable coverage enhancement.This paper proposes generalpathloss model,radiation pattern and mirror beam effect of 1-bit RIS at sub-6 GHz band.Field trails have beencarried out in outdoor and indoor deployment scenarios.The proposed model is validated through extensivesimulations and field-trial measurements.In addition,an optimized RIS phase-shit design process for the mirrorbeam elimination is proposed and validated with simulations.The proposed theoretical model and measurementresults can promote future research and application in RIS-assisted communications.

A multi-purpose prototype test system for mechanical behavior of tunnel supporting structure: Development and application

A multi-purpose prototype test system is developed to study the mechanical behavior of tunnel sup-porting structure,including a modular counterforce device,a powerful loading equipment,an advanced intelligent management system and an efficient noncontact deformation measurement system.The functions of the prototype test system are adjustable size and shape of the modular counterforce structure,sufficient load reserve and accurate loading,multi-connection linkage intelligent management,and high-precision and continuously positioned noncontact deformation measurement.The modular counterforce structure is currently the largest in the world,with an outer diameter of 20.5 m,an inner diameter of 16.5 m and a height of 6 m.The case application proves that the prototype test system can reproduce the mechanical behavior of the tunnel lining during load-bearing,deformation and failure processes in detail.

Numerical study of geyser events in rainstorm systems at different scales

Considering that we still do not fully understand the behavior of air pockets trapped in rainstorm systems and water flow changes inside pipes,the study of actual geysers presents many challenges.In this study,three-dimensional numerical models were developed to investigate the mechanisms of geyser events triggered by rapid filling flows at different scales.The results showed that,in the first stage of the water–air mixture of the prototype model,a large amount of air was released quickly,and the subsequent overflow lasted for a more extended period.The transport capacity of the downstream pipe,as a critical factor,significantly influenced the water–air interaction of the geyser.Restricting the outlet area and increasing the outlet pressure simultaneously resulted in a stronger geyser.The equivalent density of the water–air mixture increased as the scale decreased during the geyser event.

Development and preliminary results of a large-pixel two-layer LaBr_(3) Compton camera prototype

Lanthanum bromide(LaBr_(3))crystal has a high energy resolution and time resolution and has been used in Compton cameras(CCs)over the past few decades.However,LaBr_(3) crystal arrays are difficult to process because LaBr_(3) is easy to crack and break;thus,few LaBr_(3)-based CC prototypes have been built.In this study,we designed and fabricated a large-pixel LaBr_(3) CC prototype and evaluated its performance with regard to position,energy,and angular resolution.We used two 10×10 LaBr_(3) crystal arrays with a pixel size of 5 mm×5 mm,silicon photomultipliers(SiPMs),and corresponding decoding circuits to construct our prototype.Additionally,a framework based on a Voronoi diagram and a lookup table was developed for list-mode projection data acquisition.Monte Carlo(MC)simulations based on Geant4 and experiments were conducted to evaluate the performance of our CC prototype.The lateral position resolution was 5 mm,and the maximum deviation in the depth direction was 2.5 and 5 mm for the scatterer and absorber,respectively.The corresponding measured energy resolu-tions were 7.65%and 8.44%,respectively,at 511 keV.The experimental results of ^(137)Cs point-like sources were consistent with the MC simulation results with regard to the spatial positions and full widths at half maximum(FWHMs).The angular resolution of the fabricated prototype was approximately 6°when a point-like ^(137)Cs source was centrally placed at a distance of 5 cm from the scatterer.We proposed and investigated a large-pixel LaBr_(3) CC for the first time and verified its feasibility for use in accurate spatial positioning of radiative sources with a high angular resolution.The proposed CC can satisfy the requirements of radiative source imaging and positioning in the nuclear industry and medical applications.

Affinity-Engineered Flexible Scaffold toward Energy-Dense, Highly Reversible Na Metal Batteries

The practical deployment of metallic anodes in the energy-dense batteries is impeded by the thermodynamically unstable interphase in contact with the aprotic electrolyte,structural collapse of the substrates as well as their insufficient affinity toward the metallic deposits.Herein,the mechanical flexible,lightweight(1.2 mg cm^(−2))carbon nanofiber scaffold with the monodispersed,ultrafine Sn_(4)P_(3) nanoparticles encapsulation(Sn_(4)P_(3)NPs@CNF)is proposed as the deposition substrate toward the high-areal-capacity sodium loadings up to 4 mAh cm^(−2).First-principles calculations manifest that the alloy intermediates,namely the Na_(15)Sn_(4) and Na_(3)P matrix,exhibit the intimate Na affinity as the“sodiophilic”sites.Meanwhile,the porous CNF regulates the heterogeneous alloying process and confines the deposit propagation along the nanofiber orientation.With the precise control of pairing mode with the NaVPO4F cathode(8.7 mg cm^(−2)),the practical feasibility of the Sn_(4)P_(3) NPs@CNF anode(1^(*)Na excess)is demonstrated in 2 mAh single-layer pouch cell prototype,which achieves the 95.7%capacity retention for 150 cycles at various mechanical flexing states as well as balanced energy/power densities.

Semi-Supervised Clustering Algorithm Based on Deep Feature Mapping

Clustering analysis is one of the main concerns in data mining.A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other.Therefore,measuring the distance between sample points is crucial to the effectiveness of clustering.Filtering features by label information and mea-suring the distance between samples by these features is a common supervised learning method to reconstruct distance metric.However,in many application scenarios,it is very expensive to obtain a large number of labeled samples.In this paper,to solve the clustering problem in the few supervised sample and high data dimensionality scenarios,a novel semi-supervised clustering algorithm is proposed by designing an improved prototype network that attempts to reconstruct the distance metric in the sample space with a small amount of pairwise supervised information,such as Must-Link and Cannot-Link,and then cluster the data in the new metric space.The core idea is to make the similar ones closer and the dissimilar ones further away through embedding mapping.Extensive experiments on both real-world and synthetic datasets show the effectiveness of this algorithm.Average clustering metrics on various datasets improved by 8%compared to the comparison algorithm.

UAV-Aided Data and Energy Integrated Network: System Design and Prototype Development

Terminal devices deployed in outdoor environments are facing a thorny problem of power supply.Data and energy integrated network(DEIN)is a promising technology to solve the problem,which simultaneously transfers data and energy through radio frequency signals.State-of-the-art researches mostly focus on theoretical aspects.By contrast,we provide a complete design and implementation of a fully functioning DEIN system with the support of an unmanned aerial vehicle(UAV).The UAV can be dispatched to areas of interest to remotely recharge batteryless terminals,while collecting essential information from them.Then,the UAV uploads the information to remote base stations.Our system verifies the feasibility of the DEIN in practical applications.

Semantic Changes and Translation of Chinese Buzzwords of COVID-19 Pandemic

As a unique cultural product of the pandemic era,the COVID-19 pandemic buzzwords fully reflect the characteristics of the time.The paper applies the prototype theory to classify the semantic changes of the pandemic prevention buzzwords into two types:semantic evolution and semantic variation,and briefly discusses the implications of the prototype theory for translating the pandemic prevention propaganda slogans based on understanding the semantic changes to help people further understand the pandemic buzzwords as well as related social realities.

Simulation Analysis of Electromechanical Coupling for Unmanned Aerial Vehicle Cabin Door System

In order to study the dynamic response of the unmanned aerial vehicle cabin door opening and closing system under impact load conditions, considering the flexible treatment of mechanical components, and the system’s motion with different stiffness of energy-absorbing components, a rigid-flexible coupling model of the cabin door actuation system was established in LMS. Virtual. Motion. In Amesim, a control model of the motor was created. Through the Motion-Amesim co-simulation module, the dynamic module of the system was combined with the motor control module to complete the electromechanical coupling simulation and analyze the results. .

A Study on Second Language Vocabulary Acquisition Under the Categorization Theory

In cognitive linguistics,debates on the status and functions of categorization have been a heated issue.In semantics and second language acquisition,scholars have discussed and achieved vocabulary acquisition from different perspectives and academic levels.Vocabulary learning exerts a fundamental role in second language vocabulary acquisition(SLVA),and it is closely related to learners’cognitive competence.However,studies on second language vocabulary acquisition under the categorization theory in cognitive linguistics have received less attention from linguists when compared with other studies.This paper employs two representative dimensions,the basic-level effect and the prototype effect,under the categorization theory to further delve into the implications on second language vocabulary acquisition.This article first provides a comprehensive introduction to the nature and the approaches of the categorization theory,and then analyzes the relations and implications for second language vocabulary acquisition under the categorization theory from the perspective of the basic-level and the prototype effects.The research results showed that the basic-level effect on SLVA is mainly on the classification of word categories distinguished from the superordinate and subordinate categories,while the prototype effect is more on understanding the complexity and use of word meaning.

基于Rifidi平台下的一种RFID端到端解决方案

RFID是一项无线电频率识别技术,使软件系统可以自动的探测和识别在现实中的物体。由于这种能力,RFID受到很多公司的热捧;可惜的是涉及到RFID一个新的业务的发展的投资方案,需要在时间、硬件和基础设施方面的重大投资。相反,Rifidi是一个工具,能够迅速和切实地进行RFID情景模拟,以探讨其发展的可能性在进行投资之前。而Prototyper是一款基于RifidiEmulator的仿真软件,仿真阅读器可以被创建并被放置在一个代表工厂和仓库的蓝图上,而且各种被创建的标签可以随意的拖动,远离或接近热点。这将会使你更容易地测试工作流程和业务逻辑,而不需要昂贵的硬件设置。任何问题都可以在这款软件上发现,并得以纠正。