Cluster based hierarchical resource searching model in P2P network

For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.

Research on the User Interest Modeling of Personalized Search Engine

At present, how to enable Search Engine to construct user personal interest model initially, master user＇s personalized information timely and provide personalized services accurately have become the hotspot in the research of Search Engine area. Aiming at the problems of user model＇s construction and combining techniques of manual customization modeling and automatic analytical modeling, a User Interest Model （UIM） is proposed in the paper. On the basis of it, the corresponding establishment and update algorithms of User lnterest Profile （UIP） are presented subsequently. Simulation tests proved that the UIM proposed and corresponding algorithms could enhance the retrieval precision effectively and have superior adaptability.

Euler Characteristic Scheme of Globally Searching for Flaws in Surface Modeling

This paper presents a new scheme of flaw searching in surface modeling based on Euler Characteristic. This scheme can be applied to surface construction or reconstruction in computer. It is referred to as Euler Accompanying Test (EAT) algorithm in this paper. Two propositions in algebraic topology are presented, which are the foundation of the EAT algorithm. As the modeling is the first step for rendering in the animation and visualization, or computer-aided design (CAD) in related applications, the flaws can bring some serious problems in the final image or product, such as an artificial sense in animation rendering or a mistaken product in industry. To verify the EAT progressive procedure, a three-dimensional (3D) stamp model is constructed. The modeling process is accompanied by the EAT procedure. The EAT scheme is verified as the flaws in the stamp model are found and modified.

Algebraic or Algorithmic: Searching for Optimal Solutions in Multi-Stage Supply Chain Models

In this paper we proposed an AMH Supply Chain model to obtain optimal solutions for Two-, Three- and Four-Stage for deterministic models. Besides deriving its algebraic solutions, a simple searching method is successfully applied in obtaining optimal total costs and its integer multipliers. Our model has shown promising results in comparison to Equal Cycle Time and other existing ones. The tests focused on obtaining optimal total annual costs and other related details of Two-, Three- and Four-Stage for deterministic models. The results are run under Visual Basic Programming platform using Intel? CoreTM2 Duo T6500 Processor.

Free Search算法率定的Sacramento模型在东北寒旱区的应用

采用模型对比研究方法,以萨克拉门托(Sacramento,SAC)模型为研究对象,以新安江模型为参考,采用基于动物群体的自由搜索(Free search,FS)算法作为率定水文模型的优化算法,选取松花江水系和辽河水系的3个集水区为实证流域,通过对比FS算法率定的SAC模型和新安江模型在3个流域的模拟效果,验证SAC模型在东北寒旱区流域的适用性。研究结果表明,FS算法迭代计算过程简单,需要设置的算法参数较少,在率定SAC模型和新安江模型时效率较高;在相同的模拟条件下,FS算法率定的SAC模型模拟效果更好,其Nash模型效率系数高于新安江模型,表明SAC模型适用于东北寒旱区流域;但SAC模型在东北寒旱区的模拟精度还有待提升,模型尚需进一步发展和完善。

基于ElasticSearch的元数据搜索与共享平台

随着水利行业信息化的发展,针对海量、多源、异构数据的共享与发现成为行业研究的热点。本文设计与实现一种基于Elastic Search的水利元数据搜索与共享平台,提出针对水利异构数据的解决方案并对海量数据建立索引,利用多租户访问控制策略,保证用户索引数据的一致性与安全性。通过Rest服务对索引资源进行封装,提供搜索与多粒度的共享方式。应用表明,平台能够保证用户准确高效地获得水利行业数据,节约了水利单位构建搜索系统的成本。

Optimal search for moving targets with sensing capabilities using multiple UAVs

This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.

A Novel Search Engine for Internet of Everything Based on Dynamic Prediction

In recent years, with the rapid development of sensing technology and deployment of various Internet of Everything devices, it becomes a crucial and practical challenge to enable real-time search queries for objects, data, and services in the Internet of Everything. Moreover, such efficient query processing techniques can provide strong facilitate the research on Internet of Everything security issues. By looking into the unique characteristics in the IoE application environment, such as high heterogeneity, high dynamics, and distributed, we develop a novel search engine model, and build a dynamic prediction model of the IoE sensor time series to meet the real-time requirements for the Internet of Everything search environment. We validated the accuracy and effectiveness of the dynamic prediction model using a public sensor dataset from Intel Lab.

基于SPSS Modeler质量控制关键点的监控与应用

为了提高炼钢过程中数据查询、提取的工作效率,使用数据挖掘工具SPSS Modeler,对重点钢种生产质量关键点数据进行自动提取,并进一步处理分析和应用,不仅提高了数据的处理效率,同时利用关键点数据对现行质量控制标准进行优化,提高生产质量管理水平。

Review on the Work of Agriculture Ontology Research Group

This paper introduces efforts and achievements of Agriculture Ontology Service Research Group of Agricultural Information Institute of Chinese Academy of Agriculture Sciences in last 10 years. It summarizes the research on ontology construction methodology, ontology management system, ontology application and etc.

基于Free Search的植被叶面积指数Logistic模型研究

针对利用归一化植被指数NDVI反演的植被叶面积指数LAI时间分辨率不足的问题,对传统Logistic模型进行了改进,并引入自由搜索(Free Search,FS)算法率定模型参数,建立了一种能够模拟植被逐日LAI的Logistic模型。首先,以NOAA-AVHRR提供的月NDVI作为反演植被LAI的基础数据,采用简单生物圈模型(Si B2)反演不同土地覆被类型下的植被月LAI;其次,借鉴传统Logistic模型模拟作物干物质的思路建立了一种改进的Logistic模型,并将FS单个动物每步探查行走的位置向量作为模型参数值,利用FS的动物群体迁移行为识别改进Logistic模型的最优参数,从而研究改进Logistic模型对植被月LAI的模拟效果。呼兰河流域的应用结果表明,利用FS识别参数的改进Logistic模型对呼兰河流域6种植被的月LAI模拟效果较好,为模拟植被LAI的逐日变化过程提供了一种新方法。

基于麻雀搜索算法和长短期记忆神经网络的轨道交通站点客流预测

准确的短时客流预测可以为城市轨道交通的良好运营提供保障,但轨道交通的短时客流具有非线性和高随机性等特点,为了提高对短时客流的预测精度,提出将ISSA算法和LSTM模型进行组合,构建城市轨道交通短时客流预测模型.针对SSA算法收敛速度慢,容易陷入局部最优解的问题,引入黄金莱维飞行策略,通过动态调整探索者移动步长的方法,使得它在未知范围内搜索时,能够覆盖更大的范围,提高SSA算法全局搜索的能力.通过使用ISSA算法对LSTM模型的隐含层、学习率和迭代次数的神经元个数进行优化,构建ISSA-LSTM组合预测模型,用于城市轨道交通短时客流的预测.将该模型与BP、LSTM和SSA-LSTM等3种短时客流预测模型进行对比,结果表明:在针对工作日和非工作日客流的预测中,ISSA-LSTM模型预测误差最小,具有较好的预测效果.

Blog Opinion Retrieval with Generation Model and Mixture Model

Blog opinion retrieval aims to find blogs with opinionated information related to a given topic.Its main problem is to compute the opinion score,which balances topic relevance and opinion relevance.To deal with this problem a generative model deduced by a Bayesian approach is pro-posed,and an improved mixture model is proposed to estimate the opinion relevance between a blog and a given topic in our retrieval framework.Moreover,pointwise mutual information is used to expand sentiment words for different topics based on a general sentimental lexicon.The correlation between topic and candidate words is applied in the process of both expanding sentiment words and estimating sentence opinion scores.Experimental results show that the proposed approaches improve upon the state-of-the-art opinion retrieval method on TREC2010 dataset.

SNS model for providing social network channels through queries

Users can obtain the information through a basic web searching and find the answer to the questions directly,but maybe the expected answer does not exist.Besides,we do not know the update of new information in time.The online social networking services spread quickly and store many user data,but these data are worth less and may be unreliable answer to users’ questions.Users can obtain the simple answer but can not expect more additional information in knowledge question-answering(QA)system.In this paper,we design the system with the advantages of knowledge QA system,web searching and characteristics of social networking service for providing social network channel based on the query and answer without users’ contact network.The user can obtain real-time answers by the user network interested in users’ querires through the network channel of this system,get the additional information effectively and share it with others in the social network channel in this system.

Forecasting Method of Stock Market Volatility in Time Series Data Based on Mixed Model of ARIMA and XGBoost

Stock price forecasting is an important issue and interesting topic in financial markets.Because reasonable and accurate forecasts have the potential to generate high economic benefits,many researchers have been involved in the study of stock price forecasts.In this paper,the DWT-ARIMAGSXGB hybrid model is proposed.Firstly,the discrete wavelet transform is used to split the data set into approximation and error parts.Then the ARIMA(0,1,1),ARIMA(1,1,0),ARIMA(2,1,1)and ARIMA(3,1,0)models respectively process approximate partial data and the improved xgboost model(GSXGB)handles error partial data.Finally,the prediction results are combined using wavelet reconstruction.According to the experimental comparison of 10 stock data sets,it is found that the errors of DWT-ARIMA-GSXGB model are less than the four prediction models of ARIMA,XGBoost,GSXGB and DWT-ARIMA-XGBoost.The simulation results show that the DWT-ARIMA-GSXGB stock price prediction model has good approximation ability and generalization ability,and can fit the stock index opening price well.And the proposed model is considered to greatly improve the predictive performance of a single ARIMA model or a single XGBoost model in predicting stock prices.

基于改进蚁群算法的邮船舱室模块移运路径规划

针对大型邮船舱室模块运输过程中存在的移运路线长、路线混乱、舱室模块易与障碍物发生碰撞等问题,提出应用加入动态搜索模型的蚁群算法对邮船舱室模块进行路线规划,为运输舱室模块提供清晰、便捷的移运路线。对主竖区的障碍物进行分析,建立模拟实际工况的栅格地图,采用改进蚁群算法寻找移运路径。对不同位置所经过的栅格地图和蚁群数量进行动态调整。采用模拟退火算法寻找蚁群算法的参数。采用离散点分析确定移运路径的主、支通道。仿真试验结果表明,应用改进蚁群算法建立主、支通道进行舱室模块移运可有效提高舱室模块的运输效率。

基于ISSA-HKLSSVM的浮选精矿品位预测方法

针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vector Machine,HKLSSVM)的浮选过程精矿品位预测方法.首先采集浮选现场载流X荧光品位分析仪数据作为建模变量并进行预处理,建立基于最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的预测模型,以此构建新型混合核函数,将输入空间映射至高维特征空间,再引入改进麻雀搜索算法对模型参数进行优化,提出基于ISSA-HKLSSVM方法实现精矿品位预测,最后开发基于LabVIEW的浮选精矿品位预测系统对本文提出方法实际验证.实验结果表明,本文提出方法对于浮选过程小样本建模具有良好拟合能力,相比现有方法提高了预测准确率,可实现精矿品位的准确在线预测,为浮选过程的智能调控提供实时可靠的精矿品位反馈信息.

基于机器学习算法的糖尿病预测

糖尿病是一种无法根治的慢性疾病,早发现、早干预、早治疗能够延缓病情进展,提高患者的治疗效率。构建基于决策树、逻辑回归、XGBoost等六种机器学习分类算法的预测模型,实现糖尿病风险预测。该模型以皮马印第安人糖尿病数据集为研究对象,通过数据预处理、数据特征分析构建有效数据集,采用网格搜索方法进行交叉验证寻找算法的最佳参数组合,构建超参数及基于超参数的分类模型,并对模型的预测性能进行评价。实验结果表明,该模型拥有良好的糖尿病风险预测性能。

高校大学生实验室安全意识评价模型的构建

为减少实验室安全事故的发生和提高大学生的实验室安全意识,设计了实验室安全意识影响因素调查问卷和大学生实验室安全意识评价调查问卷,利用层次分析法—熵权法(AHP-EWM)主客观相结合的方法建立高校大学生实验室安全意识评价体系。通过主成分分析法(PCA)处理,再选取累计贡献率超过95%的评价指标进行布谷鸟搜索(Cuckoo Search, CS),最后结合混合优化支持向量机(SVM)建立PCA-CS-SVM大学生实验室安全意识评价模型。验证结果表明,与PCA-LS-SVM和PCA-GA-SVM模型相比,PCA-CS-SVM模型的预测值与实际值的误差较小且评价等级与实际值保持一致,具有较高的准确性和可靠性,可用于高校大学生实验室安全意识评价。