A Support Data-Based Core-Set Selection Method for Signal Recognition

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction

Hybrid data assimilation （DA） is a method seeing more use in recent hydrology and water resources research. In this study, a DA method coupled with the support vector machines （SVMs） and the ensemble Kalman filter （EnKF） technology was used for the prediction of soil moisture in different soil layers： 0-5 cm, 30 cm, 50 cm, 100 cm, 200 cm, and 300 cm. The SVM methodology was first used to train the ground measurements of soil moisture and meteorological parameters from the Meilin study area, in East China, to construct soil moisture statistical prediction models. Subsequent observations and their statistics were used for predictions, with two approaches： the SVM predictor and the SVM-EnKF model made by coupling the SVM model with the EnKF technique using the DA method. Validation results showed that the proposed SVM-EnKF model can improve the prediction results of soil moisture in different layers, from the surface to the root zone.

Real-Time Data Transmission with Data Carrier Support Value in Neighbor Strategic Collection in WSN

An efficient trust-aware secure routing and network strategy-based data collection scheme is presented in this paper to enhance the performance and security of wireless sensor networks during data collection.The method first discovers the routes between the data sensors and the sink node.Several factors are considered for each sensor node along the route,including energy,number of neighbours,previous transmissions,and energy depletion ratio.Considering all these variables,the Sink Reachable Support Measure and the Secure Communication Support Measure,the method evaluates two distinct measures.The method calculates the data carrier support value using these two metrics.A single route is chosen to collect data based on the value of data carrier support.It has contributed to the design of Secure Communication Support(SCS)Estimation.This has been measured according to the strategy of each hop of the route.The suggested method improves the security and efficacy of data collection in wireless sensor networks.The second stage uses the two-fish approach to build a trust model for secure data transfer.A sim-ulation exercise was conducted to evaluate the effectiveness of the suggested framework.Metrics,including PDR,end-to-end latency,and average residual energy,were assessed for the proposed model.The efficiency of the suggested route design serves as evidence for the average residual energy for the proposed framework.

Use of Data Mining to Support the Development of Knowledge Intensive CAD

In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.

Weight Data Fusion Based on Mutual Support Applied in Large Diameter Measurement

The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection, because of the bad environment of locale, the problem to amend the measuring error by non-uniform temperature field, and the difficulty to collimate and locate by usual method. By improving the measurement accuracy of larger axis accessories, it is useful to raise axis and hole＇s industry produce level. Because of the influence of complex environment in locale and some influential factors which are hard excluded from the large diameter measurement with multi-rolling-wheels method, the measurement results may not support or even contradict each other. To the situation, this paper puts forward a mutual support deviation distinguish data fusion method, including mutual support deviation detection and weight data fusion. The mutual support deviation detection part can effectively remove or weaken the unexpected impact on the measurement results and the weight data fusion part can get more accurate estimate result to the detected data. So the method can further improve the reliability of measurement results and increase the accuracy of the measurement system. By using the weight data fusion based on the mutual support （DFMS） to the simulation and experiment data, both simulation results and experiment results show that the method can effectively distinguish the data influenced by unexpected impact and improve the stability and reliability of measurement results. The new provided mutual support deviation distinguish method can be used to single sensor measurement and multi-sensor measurement, and can be used as a reference in the data distinguish of other area. The DFMS is helpful to realize the diameter measurement expanded uncertainty in 5 ×10^-6D or even higher when the measured axis workpiece＇s diameter is 1-5 m （ 1 m ≤ D ≤5 m ）.

Data Selection Using Support Vector Regression

Geophysical data sets are growing at an ever-increasing rate,requiring computationally efficient data selection （thinning） methods to preserve essential information.Satellites,such as WindSat,provide large data sets for assessing the accuracy and computational efficiency of data selection techniques.A new data thinning technique,based on support vector regression （SVR）,is developed and tested.To manage large on-line satellite data streams,observations from WindSat are formed into subsets by Voronoi tessellation and then each is thinned by SVR （TSVR）.Three experiments are performed.The first confirms the viability of TSVR for a relatively small sample,comparing it to several commonly used data thinning methods （random selection,averaging and Barnes filtering）,producing a 10％ thinning rate （90％ data reduction）,low mean absolute errors （MAE） and large correlations with the original data.A second experiment,using a larger dataset,shows TSVR retrievals with MAE ＜ 1 m s-1 and correlations ≥ 0.98.TSVR was an order of magnitude faster than the commonly used thinning methods.A third experiment applies a two-stage pipeline to TSVR,to accommodate online data.The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment,is an order of magnitude faster than the nonpipeline TSVR.Therefore,pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set.This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.

Multimode Process Monitoring Based on the Density-Based Support Vector Data Description

Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.

Linked Data Based Framework for Tourism Decision Support System: Case Study of Chinese Tourists in Switzerland

Switzerland is one of the most desirable European destinations for Chinese tourists;therefore, a better understanding of Chinese tourists is essential for successful business practices. In China, the largest and leading social media platform—Sina Weibo, a hybrid of Twitter and Facebook—has more than 600 million users. Weibo’s great market penetration suggests that tourism operators and markets need to understand how to build effective and sustainable communications on Chinese social media platforms. In order to offer a better decision support platform to tourism destination managers as well as Chinese tourists, we proposed a framework using linked data on Sina Weibo. Linked Data is a term referring to using the Internet to connect related data. We will show how it can be used and how ontology can be designed to include the users’ context (e.g., GPS locations). Our framework will provide a good theoretical foundation for further understand Chinese tourists’ expectation, experiences, behaviors and new trends in Switzerland.

区域经济发展Data Farming决策支持技术及其应用

为解决现有的决策支持技术不能解决的区域经济历史数据稀少的问题,提出了区域经济发展Data Farming决策支持技术。所提出的技术采用基于智能体仿真的Data Farming技术,对参数空间进行遍历搜索,以利用各个参数值对应的仿真结果来获取大量的数据,使决策者可以有效地识别到系统的最优控制值,并了解在控制值下的系统风险性。最后,以江门市社会消费品零售总额的控制优化为例,说明了该技术的有效性。

基于开放教育大数据的学生支持服务行动方案研究

终身教育的高质量发展需要为个性化学习提供有力支撑,其中,为学生提供个性化支持服务是推动学业进展的重要保障。作为服务终身教育的主力军,开放大学普遍缺少精细化掌握学生情况以提供支持服务的主动干预举措。案例校是一所省级开放大学,通过三个学期的行动研究,形成了基于开放教育大数据的学生支持服务六步骤行动方案,构建了交互式驾驶舱,呈现了每个学生的学业谱系图,实现了对学生情况的整体把控、分类规划和细节关注。通过大数据区分出遗留生、学困生、接近毕业学生等特定群体,明确学业风险预警,并提出将学生需求与利益相关者对接、形成支持服务团队进行针对性干预和帮助的建议。

浅埋偏压双洞隧道洞口段施工现场监测研究

本研究以雅安大石板隧道为工程背景,针对其进出口段位于Ⅴ级围岩浅埋偏压段的地质条件,决定采用Z5 a型衬砌为隧道支护结构。通过现场监测,对支护结构的应力进行数据分析,发现喷射混凝土和二次衬砌混凝土的应力最大值均位于靠近边坡的左线断面拱顶处,压应力最大为15.11 MPa和10.15 MPa,分别要比右线断面多15.17%和94.81%。应力曲线随时间推移趋于稳定,衬砌应力均未超出材料的极限强度,隧道结构处于安全稳定状态。

智慧智库科研与信息化平台建设规划——以福建医科大学附属第二医院为例

随着医学科研和医疗信息化的发展,构建医院智慧科研信息化平台成为提升科研管理和服务质量的关键。本文基于福建医科大学附属第二医院科研管理系统的建设经验和需求,规划了一个全面的智慧科研信息化平台,旨在加强医院科研的技术支持和决策力度。平台重点关注项目管理、专家库构建和数据深度分析,运用数据挖掘、知识图谱等技术,确保科研成果的整合与应用,旨在提升科研管理效率,增强医院政策决策参与度,为地区医疗健康水平和治理体系现代化做出贡献。

软土地区水泥土墙后插微型桩复合支护结构加固机理及应用分析

水泥土墙后插微型桩为一种较为新型的支护结构型式,目前规范中暂无该类支护结构计算方法,且在工程应用与研究中较少。为此,描述了水泥土墙内置微型桩支护体系布置形式,采用有限元数值建模计算分析方法研究了水泥土墙后插微型桩复合支护结构加固机理,提出了后插微型桩复合支护结构体系稳定性理论计算方法,并结合深圳市某软土地区堤防工程进行了应用实例计算分析及监测数据验证。结果表明,微型桩主要承受轴向荷载,直至支护体系破坏桩身弯矩和剪力仍较小,其加固作用类似于锚栓;建立的复合支护结构计算简图及稳定性计算公式可实现支护结构稳定性计算;现场监测数据的反馈可得出该新型复合支护结构体系安全可靠;水泥土墙后置深层微型桩应用于软土地区堤防工程中综合性价比高,较大提高了水泥土墙抗滑移、抗倾覆能力,降低了工程造价,具有一定的推广价值。

煤矿灾害监测预警与融合展现平台研究

灾害监测预警是智能化矿山的重点建设内容和验收要求。针对当前煤矿灾害监测预警数据接入规范不完整、地质保障系统三维模型不能共享、灾害监测预警结果无法融合展现等突出问题,制定了井巷工程地理信息、地质体及设备三维模型、灾害监测预警、灾害应急处置等数据的接入规范,研究了数据采集治理与存储、三维模型的快速构建和实时渲染、多种灾害监测预警融合展现等技术,研发了煤矿灾害监测预警与融合展现平台。现场应用表明,该平台利用已有地质保障系统建设成果,实现了基于三维模型的监测预警数据的融合展现,有效提升了煤矿灾害监测预警的表达能力,为煤矿安全生产和灾害应急处置提供了技术支撑。

Weighted Proximal Support Vector Machines: Robust Classification

Despite of its great efficiency for pattern classification, proximal supportvector machines (PSVM), a new version of SVM proposed recently, is sensitive to noise and outliers.To overcome the drawback, this paper modifies PSVM by associating a weightvalue with each input dataof PSVM. The distance between each data point and the center of corresponding class is used tocalculate the weight value. In this way, the effect of noise is reduced. The experiments indicatethat new SVM, weighted proximal support vector machine (WPSVM), is much more robust to noise thanPSVM without loss of computationally attractive feature of PSVM.

大数据分析在航空维修保障中的应用初探

航空装备运维数据分析作为服务保障体系的技术支持工程基础,旨在利用飞参数据、飞行信息、故障信息、维护信息等。本文通过数据分析处理相关技术,对其内涵特征、衍变趋势进行研究分析,建立系统设备健康衰退模型,开展数据挖掘研究,从而进行故障诊断、故障预测、健康评估和维修策略优化,实现基于数据分析的飞机故障诊断与健康评估。

基于LoRa的液压支架无线振动监测系统设计

针对顶煤开采过程中液压支架的振动测量与无线传输困难问题,设计了基于LoRa的振动无线测量传输系统。设计了基于STM32单片机和AD7606的振动采集硬件系统,实现对压电式振动传感器的信号采集。针对煤矿井下振动数据无线传输困难的问题,设计了基于LoRa的无线发送单元和接收单元,并采用串表压缩算法(Lempel Ziv Welch,LZW)对振动数据进行压缩传输,上位机接收、解压缩并分析处理数据。实验结果表明,设计的系统最多可以测量8个通道的振动信号,1024字节数据在5 dB信噪比条件下无线传输压缩率为70.46%,为液压支架的振动采集与无线传输提供了可靠的解决方案。

An iterative modified kernel based on training data

To improve performance of a support vector regression, a new method for a modified kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thus the kernel function is data-dependent. With a random initial parameter, the kernel function is modified repeatedly until a satisfactory result is achieved. Compared with the conventional model, the improved approach does not need to select parameters of the kernel function. Sim- ulation is carried out for the one-dimension continuous function and a case of strong earthquakes. The results show that the improved approach has better learning ability and forecasting precision than the traditional model. With the increase of the iteration number, the figure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm.