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 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.

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.

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.

基于K-prototypes的混合属性数据聚类算法改进

属性数据分为数值型数据和分类型数据,一般情况下对于数值型数据运算前要进行标准化处理,但是对于数值型数据差异大的数据,由于大数掩盖小数的影响,按照K-prototypes聚类算法,数值型数据标准化后而且不对相应的分类数据有任何预处理或者在计算时没有进行任何改变,很可能提高分类数据在聚类中的影响,并且分类型数据并未进一步地细分,不能满足不同要求的混合属性聚类。该文在将数值型数据标准化的基础上,将分类数据细分为二元数据和类型数据,并用相异度系数距离计算分类数据之间的距离,并且赋予二元和类型数据相应的权重,来改进K-prototypes聚类算法,使该算法满足不同要求的混合属性数据聚类,最后通过C#语言,在ArcEngine2010版本上实现。

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.

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.

基于在线梯度下降的Mini Batch K-Prototypes算法

K-Prototypes算法每次迭代都需要花费大量时间来计算所有样本与每个簇中心的相异度以将其划分到各个聚类簇中,这导致K-Prototypes算法在处理大型数据集时运行时间急剧增加。根据在线梯度下降算法对K-Prototypes算法的迭代过程进行优化,减少算法每次迭代所需的计算量从而降低算法的时间复杂度,提升算法运行效率。实验结果表明,提出的基于在线梯度下降的K-Prototypes聚类算法,可以在不影响算法收敛性和有效性的前提下降低算法的时间复杂度,提升算法运行效率。

基于改进K-prototypes与GBDT的城市干道车辆出行群体辨识模型

为了掌握城市干道交通运行规律,向交通管理部门制定相关交通需求管理政策提供理论依据,提出了一种基于组合模型的城市干道车辆出行群体辨识模型。基于青岛市胶州湾隧道过车数据,从出行强度、出行时间与出行习惯3个维度构建了出行特征指标体系以全面刻画车辆个体的出行行为。基于相关性分析剔除了冗余指标以避免对辨识研究的影响。针对混合属性出行特征指标数据,使用改进K-prototypes算法以有效地实现车辆出行群体划分,将其与GBDT算法相结合,建立了一种基于改进K-prototypes与GBDT的辨识模型,随机选取10000个样本开展辨识研究。结果表明:研究道路存在5类车辆出行群体:高频通勤群体、低频通勤群体、营运群体、频次稳定群体与普通群体,对于这5类车辆出行群体,平均识别准确率为97.75%,最高识别准确率可达99.47%。

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.

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.

PARAMETERIZED-PROTOTYPE-BASED DYNAMIC OPTIMIZATION DESIGN FOR CAM PROFILE

Aiming at the problems in current cam profile optimization processes, such as simple dynamics models, limited geometric accuracy and low design automatization level, a new dynamic optimization mode is put forward. Based on the parameterization modeling technique of MSC. ADAMS platform, the different steps in current mode are reorganized, thus obtaining an upgraded mode called the ＂parameterized-prototype-based cam profile dynamic optimization mode＂. A parameterized prototype（PP） of valve mechanism is constructed in the course of dynamic optimization for cam profiles. Practically, by utilizing PP and considering the flexibility of the parts in valve mechanism, geometric accuracy and design automatization are improved.

Experimental Investigation on Basic Prototype of Solid Propellant Impulsive Microthrusters

A new type of impulsive microthruster and its measurement system were designed for the aim of testing the performance of a basic prototype of solid propellant impulsive microthruster. Two sets of tests were conducted. The tests show that the ignitor and the main charge of the microthruster match well, the dynamic and static capability of the test and measurement meets the test requirement and the result is creditable. The measured technical characteristics of the microthruster are that the ignition delay time is shorter than 0 3?ms, the total impulse is over 3?N·s, the operational time is shorter than 16?ms and the mass ratio of the thruster is 0 216.

基于K-prototypes的混合属性数据聚类算法

通过对基于K-prototypes算法对混合属性数据处理的聚类问题进行研究,改进了K-prototypes算法中分类属性相异度计算公式,使之能更加精确反映样本间的差异;在此基础上提出了一种用于处理混合属性数据的聚类算法,并将改进后的算法应用于英语借词数据的聚类分析中。实验结果表明,与K-prototypes算法相比,改进后的算法具有更好的稳定性和更高的精度。

一种改进的加权K-prototypes算法

为了提高K-prototypes算法的聚类准确度,解决其随机选取聚类中心初始值导至的聚类精度较低和聚类结果不稳定的问题。通过对混合属性数据聚类算法的研究,对K-prototypes算法做了进一步的改进。提出了混合属性聚类的初始聚类中心确定方法,并且通过加权算法改进了相异度计算公式。最后用UCI数据集对算法进行检验,结果表明,改进的加权K-prototype算法更加稳定,并具有较高的聚类精度。

一种增强的K-prototypes混合数据聚类算法

针对K-prototypes算法处理混合数据时精度不高等问题,提出了一种增强的K-prototypes混合数据聚类算法(An Enhanced K-prototypes Mixed Data Clustering Algorithm,EKPCA).首先定义了一种新的距离计算公式,扩大了数据之间的差异性,有利于对簇边缘数据进行合理划分;其次选取较多初始原型来覆盖数据的整体信息;最后迭代消去多余原型,得到数据集的真实分类.在8个UCI数据集上对算法进行评测,实验结果表明EKPCA算法有较高聚类精度.

基于K-prototype聚类的学生教育画像分析

针对包含类别数据与数值数据的高校教育大数据,采用基于汉明距离与欧式距离混合度量的K-prototype进行聚类,并通过轮廓系数对聚类效果进行评价,与常用的k-means、DBSCAN进行对比,实验结果表明:基于K-prototype的聚类在教育大数据的处理上更具优势。基于聚类结果,采用词云图构建了4类学生教育画像。画像从基本属性、生活消费、生活规律等维度将学生群体划分为“思想积极型学生”“夜猫子游戏型学生”“规律作息型学生”“节俭好学型学生”4类,为学生教育管理人员提供相应的决策支持。

Questionnaire Design and Analysis for the Influences of Prototype Landscape on One's Landscape Perception

Prototype landscape refers to the impressive scenes that one has experienced in his/her living environment before 20 years old.Based on the analysis of the existing literature,the authors compiled a standard scale type questionnaire by means of a field survey,which was about the influences of prototype landscape on one's landscape perception.Taking Likert scale as the main part,this questionnaire analyzed the influence of prototype landscape on landscape perception from perception,attitude,and behavior dimensions.In order to further improve its rationality,the authors tested some other aspects of this questionnaire,including logic validity,construct validity,congeniality reliability,split-half reliability,etc..The results validated that the questionnaire possessed good theoretical structure and validity target,which can evaluate various aspects of prototype landscape on one's landscape perception in an effective and reliable way.Therefore,the questionnaire put forward by this study not only enriched the studies of prototype landscape on landscape designing,but also provided an effective tool for quantitative analysis of "the influences of prototype landscape on one's landscape perception".

模糊k-prototypes聚类算法的一种改进算法

模糊k-prototypes算法是当前聚类分析中最有效算法之一.简述了模糊k-prototypes算法的发展进程和主要性质;并在此基础上,指出它在处理数值型和分类型混合数据时的不足,进而提出一种改进算法;最后,将算法应用到英语借词之中,给出计算结果.结果表明,改进算法具有较好的稳定性和较高的精确度.

一种基于k-prototype的多层次聚类改进算法

针对k-prototype算法在处理复杂的数据集时,常出现一些纯度不高的簇,影响了聚类质量的问题,提出一种基于k-prototype的多层次聚类改进算法,利用属性自动选择的方法将一些纯度不高的簇进行再聚类,以提高聚类质量.以UCI标准测试数据集进行实验,实验结果表明,该改进算法能够明显提高混合型数据集的聚类质量,并且在数据约简方面有良好表现.