基于重叠信息的基因组测序短片段定位算法

提出了一种新的测序短片段定位算法Umap,算法引入核心片段逐步扩展延伸的基本思想,通过短片段间的重叠信息定位短片段.首先找出所有在参考基因组上只出现一次的短片段,称为唯一短片段.然后以唯一短片段为基础,利用短片段间的重叠信息,使用贪婪算法对唯一短片段进行扩展,进而确定其他非唯一短片段的准确位置.实验表明,该算法对短片段的定位比现有短片段定位算法更加准确,能够定位的短片段数目更多,匹配的短片段比率达到71%.通过利用客观存在于短片段间的重叠信息,可以更加准确地在参考基因组上对短片段在参考基因组上进行定位,减少模糊匹配.     

一种基于卷积神经网络的CSI指纹室内定位方法

针对提高Wi-Fi指纹室内定位技术性能,提出了一种基于卷积神经网络（Convolutional neural networks,CNN）的信道状态信息（Channel state information,CSI）指纹室内定位方法。在离线阶段联合定位环境参考点的幅度差和相位差信息,利用CNN进行训练,保存训练后的CNN网络模型作为指纹;在线阶段,针对不同实验场景,对测试数据的幅度差信息和相位差信息进行加权处理,引入改进的基于概率的指纹匹配算法,利用待定位点的CSI信息并通过CNN网络模型预测待定位点的坐标。此外,为增强算法普适性,针对复杂室内场景,提出了双节点定位方案来提高定位精度。在廊厅和实验室室内两种不同定位场景进行了实验,信息联合定位算法分别获得了24.7 cm和48.1 cm的平均定位误差,验证了基于CNN的CSI幅度差和相位差联合定位算法的有效性。      

基于模糊神经网络的短时公交到站时间预测

研究了中国公交运营特点,利用全球定位系统和电子票务收费系统收集的车辆实时信息,建立了路段和站点补偿模糊神经网络模型,分别预测车辆的路段行驶时间和站点停留时间.路段预测模型的输入是所有重合线路的站点行驶数据,改变了现有预测模型只采用单线路数据的不足.以济南市一条实际公交线路为例,利用VISSIM模拟专用道和非专用道两种道路结构并计算到站时间预测值,结果证明:提出的模型性能明显优于平均值法和卡尔曼滤波法,15min内预测累积误差小于10%,而在公交专用道上误差小于7%.     

改进模糊神经网络的自主导向车路径规划

自主导向车(AGV)在工业中得到广泛的应用,包括机械加工、原料及产品运输和冶金自动化等.在AGV路径规划中,为弥补传统模糊神经网络的不足,提出了补偿模糊神经网络算法;依据碰撞危险度的概念,为该网络制定一个折中的方案.最后给出了有静态和动态障碍物的环境中路径规划的仿真,验证了该算法提高收敛速度的有效性和避障的可行性.     

一种改进的等误差自组织特征映射矢量量化算法

为了改善矢量量化的码书性能和提高神经网络的学习效率,在分析等误差自组织特征映射算法(equidistortion self-organizing feature mapping,EDSOFM)的基础上,提出了一种改进算法.改进算法将模糊神经网的隶属度函数引入到竞争学习算法中,有效地提高了学习收敛速度.针对原算法搜索获胜码字时计算量较大的问题,改进算法通过不等式判决的方法,快速排除了大量的不匹配码字.实验结果表明,改进算法使码书设计的计算量得到明显的减少,而且码书的性能得到了提高.     

无线传感器网络中最优分割圆定位算法

为提高无需测距定位算法精度,提出一种最优分割圆定位(OPCL)算法.以节点通信半径做分割圆,将待定位节点附近的锚节点分割为圆内及圆外两部分,定义匹配函数以量化衡量分割结果与实际一、两跳锚节点集合的匹配程度.在一定搜索范围和搜索粒度下,取最优分割圆的圆心作为目标位置.算法综合利用了节点连通性约束及非连通性约束.仿真结果显示,与同类的质心算法、最小包含圆算法相比定位精度显著提升,尤其是对边缘节点定位效果更明显.     

FALCON模糊神经网络及其在铝电解槽阳极效应预报中的应用

FALCON(Fuzzy Adaptive Learning CONtrol Network)模糊神经网络是一种结合模糊理论和神经网络技术而成的人工神经网络模型,可应用于模式识别和故障预测等领域,尤其是在铝电解工业中,FALCON模糊神经网络能够有效地对铝电解槽的阳极效应进行预报.本文研究了FALCON模糊神经网络结构及其算法,并将其用于铝电解槽的工作状态监测和预报.     

基于补偿模糊神经网络的自主导向车路径规划

针对常规模糊神经网络的不足,提出了补偿模糊神经网络算法的自主导向车路径规划方法。该网络对介于最坏和最好输入的情况制定一个相对折中的方案。最后给出了有障碍物的环境中路径规划的仿真结果,结果表明此方法是可行的,并能有效地提高算法的收敛速度。     

神经网络控制系统在钛渣电炉上的应用

针对大型钛渣电炉生产中炉压、一氧化碳含量、电流等参数属于非线性变量的状况,提出一种类似于标准的操作方法,基于模糊神经网络的智能协调控制方案,应用提前算法对模糊神经网络结构和参数进行优化,并采用神经网络模块与PLC的逻辑梯形图语言编程实现智能协调运算,实际应用后,冶炼时间缩短,电耗降低.     

基于PML及Hedge的物联网异构信息集成处理模型

针对物联网前端感知信息的海量性和异构性问题,提出物联网异构信息集成处理体系结构,包括引入物联网异构数据交换标准并提出物联网异构信息处理方法.物联网异构信息集成方案在感知层、传输层及应用层进行.在感知层,模型对不同传感器或RFID的信息进行归一化及数据清洗;在传输层和应用层,模型对这些异构物联网信息进行信息融合及智能处理.提出了新的基于Hedge文法的高效过滤算法以提高匹配引擎处理海量数据的能力.     

Predicting the Endpoint Phosphorus Content of Molten Steel in BOF by Two-stage Hybrid Method

A two-stage hybrid method is proposed to predict the phosphorus content of molten steel at the endpoint of steelmaking in BOF (Basic Oxygen Furnace). At the first clustering stage, the weighted K-means is performed to produce clusters with homogeneous data. At the second predicting stage, each fuzzy neural network is carried out on each cluster and the results from all fuzzy neural networks are combined to be the final result of the hybrid method. The hybrid method and single fuzzy neural network are compared and the results show that the hybrid method outperforms single fuzzy neural network.     

Bridging GPS outages of tightly-coupled GPS/SINS using GMDH neural network

A tightly-coupled GPS (global positioning system)/SINS (strap-down inertial navigation system) based on a GMDH (group method of data handling) neural network was presented to solve the problem of degraded accuracy for less than four visible GPS satellites with poor signal quality.Positions and velocities of the satellites were predicted by a GMDH neural network,and the pseudo-ranges and pseudo-range rates received by the GPS receiver were simulated to ensure the regular operation of the GPS/SINS Kalman filter during outages.In the mathematical simulation a tightly-coupled navigation system with a proposed approach has better navigation accuracy during GPS outages,and the anti-jamming ability is strengthened for the tightly-coupled navigation system.     

Intelligent Multivariable Modeling of Blast Furnace Molten Iron Quality Based on Dynamic AGA-ANN and PCA

Blast furnace(BF)ironmaking process has complex and nonlinear dynamic characteristics.The molten iron temperature(MIT)as well as Si,P and S contents of molten iron is difficult to be directly measured online,and large-time delay exists in offline analysis through laboratory sampling.A nonlinear multivariate intelligent modeling method was proposed for molten iron quality(MIQ)based on principal component analysis(PCA)and dynamic genetic neural network.The modeling method used the practical data processed by PCA dimension reduction as inputs of the dynamic artificial neural network(ANN).A dynamic feedback link was introduced to produce a dynamic neural network on the basis of traditional back propagation ANN.The proposed model improved the dynamic adaptability of networks and solved the strong fluctuation and resistance problem in a nonlinear dynamic system.Moreover,a new hybrid training method was presented where adaptive genetic algorithms(AGA)and ANN were integrated,which could improve network convergence speed and avoid network into local minima.The proposed method made it easier for operators to understand the inside status of blast furnace and offered real-time and reliable feedback information for realizing close-loop control for MIQ.Industrial experiments were made through the proposed model based on data collected from a practical steel company.The accuracy could meet the requirements of actual operation.     

Estimation of Aeroelastic Parameters of Bridge Decks Using Neural Networks

A new method of estimating flutter derivatives using artificial neural networks is proposed. Unlike other computational fluid dynamics based numerical analyses, the proposed method estimates flutter derivatives utilizing previously measured experimental data. One of the advantages of the neural networks approach is that they can approximate a function of many dimensions. An efficient method has been developed to quantify the geometry of deck sections for neural network input. The output of the neural network is flutter derivatives. The flutter derivatives estimation network, which has been trained by the proposed methodology, is tested both for training sets and novel testing sets. The network shows reasonable performance for the novel sets, as well as outstanding performance for the training sets. Two variations of the proposed network are also presented, along with their estimation capability. The paper shows the potential of applying neural networks to wind force approximations.     

Neural Network Model for Estimating Construction Productivity

The paper describes an approach developed to estimate construction productivity for concrete formwork tasks. The system utilizes artificial neural networks, historical information, and input from experienced superintendents employed by a leading construction general contractor. It also summarizes a study undertaken to determine factors that affect labor productivity, the survey conducted to collect relevant data, and the design, training, and implementation of artificial neural networks at the participating company. A number of alternative neural network structures were investigated, the adopted one was a three-layered network with a fuzzy output structure. It was found that this structure provided the most suitable model since much of the input was subjective. A brief overview of the computer implementations and the overall experience with the system development is also provided. The method was compared to an existing statistical model developed by the same contractor and was found to improve the quality of the estimates attained. A case study conducted in the context of a workshop with senior estimators is also presented.     

小波模糊神经网络在高炉铁水硅质量分数预测中的应用

在运用模糊神经网络进行预测的基础上,建立了一种应用小波理论对时间信号进行去噪,根据去噪处理对模糊神经网络作相应处理的预测模型,并将所建模型应用于预测高炉铁水中硅的质量分数.仿真结果表明小波模糊神经网络比模糊神经网络更具优越性,预测准确率明显提高.     

Preliminary Design System for Concrete Box Girder Bridges

A neural network-based design system is presented in this paper for preliminary design of concrete box girder bridges. The system is based on a loose coupling model that integrates the artificial neural network and the fuzzy network to perform the task of noisy data filtering, knowledge extraction, and candidate synthesis. After a comparative study, the radial basis function neural network is chosen in the design knowledge generation instead of the commonly used back-propagation neural network. The fuzzy network is employed to determine the integer types of design parameters. The developed system provides a few feasible design configurations, and enables the user to overwrite some of the design parameters, so that that user can have a wide choice in his preliminary design. The accuracy of the neural network testing and the influence of the size of the design cases on the neural network prediction are discussed. A design example is included to illustrate the design procedure.     

基于模糊聚类的BP神经网络模型预报中厚板轧制力

以实测数据为基础,在中厚板轧制设定中采用BP神经网络的方法取代传统的轧制力数学模型,并对神经网络输入项和训练样本进行分析,将传统轧制力模型的自学习过程引入神经元网络用于轧制力预报,改善预报精度.采用模糊聚类分析方法,科学选取学习样本,解决了由于样本多学习速度慢的问题.通过在线数据分析,可知这种方法对轧制力的预报精度有很大改善,而且神经元网络的结构也得到简化.此方法可以作为神经元网络应用的一个拓展.     

Using Soft Computing to Analyze Inspection Results for Bridge Evaluation and Management

The national bridge inventory (NBI) system, a database of visual inspection records that tallies the condition of bridge elements, is used by transportation agencies to manage the rehabilitation of the aging U.S. highway infrastructure. However, further use of the database to forecast degradation, and thus improve maintenance strategies, is limited due to its complexity, nonlinear relationship, unbalanced inspection records, subjectivity, and missing data. In this study, soft computing methods were applied to develop damage prediction models for bridge abutment walls using the NBI database. The methods were multilayer perceptron network, radial basis function network, support vector machine, supervised self-organizing map, fuzzy neural network, and ensembles of neural networks. An ensemble of neural networks with a novel data organization scheme and voting process was the most efficient model, identifying damage with an accuracy of 86%. Bridge deterioration curves were derived using the prediction models and compared with inspection data. The results show that well developed damage prediction models can be an asset for efficient rehabilitation management of existing bridges as well as for the design of new ones.