Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.     

MD-UNET: Multi-input dilated U-shape neural network for segmentation of bladder cancer

Accurate segmentation of the tumour area is crucial for the treatment and prognosis of patients with bladder cancer. However, the complex information from the MRI image poses an important challenge for us to accurately segment the lesion, for example, the high distinction among people, size of bladder variation and noise interference. Based on the above issues, we propose an MD-Unet network structure, which uses multi-scale images as the input of the network, and combines max-pooling with dilated convolution to increase the receptive field of the convolutional network. The results show that the proposed network can obtain higher precision than the existing models for the bladder cancer dataset. The MD-Unet can achieve state-of-art performance compared with other methods.     

基于质心迭代估计的无线传感器网络节点定位算法

针对无线传感器网络非测距定位方法的应用,提出了基于质心迭代估计的节点定位算法.该算法首先计算当前连通信标节点所围成的平面质心的坐标及其与未知节点间的接收信号强度,然后用计算所得质心节点替代距离未知节点最远的连通信标节点,缩小连通信标节点所围成的平面,并通过多次迭代的方法提高节点定位精度.仿真实验结果表明,该算法的各项指标均为良好,适用于无线传感器网络的节点定位.     

基于联合分步APIT定位算法的改进

本文研究了无线传感网络( Wireless Sensor Network,WSNs)的节点定位问题,并针对APIT由于锚节点在低密度环境下的节点误判和节点失效等问题给出了改进,在APICT定位算法的基础提出了联合分步定位算法UNION-APICT(Union Approximate Point-In-Circumcircle Test),该算法是结合连通性的测距技术,RSSI测距技术以及质心定位和APICT等技术,来联合解决对未知节点定位问题。通过仿真实验结果表明,改进后的UNION-APICT在APICT算法的基础之上平均定位误差减少了10%-25%,定位性能有了明显的提升;随着通信半径R和最大探测距离rmax的增加,定位误差也在逐渐减小,该算法较APIT和APICT定位算法在锚节点密度、节点覆盖率和定位精度上都有所提高。     

基于神经网络的NMR谱图自动相位校正综合算法

目前,已有多种算法被应用在核磁共振谱图自动相位校正中,由于各种算法本身特性和所基于谱图的具体特性的差异,不同算法对于特征不同的谱图的适用性也各不相同. 针对这一情况,文在综合研究多种现有自动相位校正算法的基础上,提出了一种基于神经网络的,可以根据谱图的特征来选取最合适的算法进行自动相位校正的综合算法. 实验表明,本算法可以获得比以往方法更好的计算结果.     

Exploring artificial neural networks to model interatomic and intermolecular potential energy surfaces

We demonstrate how a back-propagation artificial neural network can be trained to represent a potential energy surface (PES) in a formless manner with limited data points and exploited to predict interaction energies for configurations not included in the training set. A similar exercise is undertaken for predicting the eigenvalues and eigenvectors of a model Hamiltonian matrix that delicately depends on parameters and exhibits crossing of eigen values.     

Morphology controllable conjugated network polymers based on AIE-active building block for TNP detection

Luminescent conjugated network polymer is one of the most promising chemo-sensors owing to their good chemical/optical stability and multiple functionalization.Herein,three conjugated network polymers were prepared by using aggregation-induced emission active 1,1,2,2-tetrakis(4-formyl-(1,1'-biphenyl))-ethane(TFBE) unit as monomer and hydrazine as linker.Through regulating the synthetical condition,the polyme ric network can form either unifo rm two-dimensional azine-linked nanosheets(ANS),conjugated microporous polymers(A-CMP) or covalent organic frameworks(A-COF).All of these polymers exhibited good stability and high fluorescence quantum efficiency with the quantum yield of6.31% for A-NS,5.26% for A-CMP,and 5.80% for A-COF,as well as fast and selective fluorescence quenching response to 2,4,6-trinitrophenol(TNP).And the best TNP sensing performance with the Stern-Volmer constants(K_(sv)) values up to 8 × 10~5 L/mol and a detection limit of 0.09 μmol/L was obtained for A-NS.The study explores various strategies to construct conjugated polymers with different nanoarchitectures based on the same building block for sensitive detection of explosives.     

Year Discrimination of Mild Aroma Chinese Liquors Using Three-Dimensional Fluorescence Spectroscopy Combined with Parallel Factor and Neural NetworkSCIEI北大核心CSCD

Three-dimensional fluorescence spectroscopy coupled with parallel factor analysis and neural network was applied to the year discrimination of mild aroma Chinese liquors. The excitation-emission fluorescence matrices (EEMs) of 120 samples with various years were measured by FLS920 fluorescence spectrometer. The trilinear decomposition of the data array was performed and the loading scores of and the excitation-emission profiles of four components were also obtained. The scores were employed as the inputs of the BP neural networks and the PARAFAC-BP identification model was constructed. 10 samples were collected from 10, 20 and 30 years of liquors respectively, and 30 samples were selected as the test sets. The remaining 90 samples were used as the training sets to build the training model. The year prediction of unknown samples was also carried out, and the prediction accuracy was 90%, 100% and 100%, respectively. Meanwhile, the discrimination analysis method and the multi way partial least squares discriminant analysis were compared, namely PARAFAC-BP and NPLS-DA. The results indicated that parallel factor combined with the neural network (PARAFAC-BP) has higher prediction accuracy. The proposed method can effectively extract the spectral characteristics, and also reduce the dimension of the input variables of neural network. A good year discrimination result was finally achieved.     

基于BDS/GPS和百度地图的现代有轨电车监控系统的设计

为实现对现代有轨电车车辆的位置及运行状态进行实时监控,设计了一个基于BDS/GPS和百度地图的现代有轨电车监控系统;定位终端以具有我国自主知识产权的BDS/GPS双模定位模块为主,RFID系统作为辅助定位手段;数据通过HTTP的GET方法提交至服务器,由服务器端的PHP页面自动处理后存储到MySQL数据库,中心管理平台显示页面调用百度地图API,将数据显示在百度地图上;最终该系统可以实时获取并存储列车的位置坐标、速度、时间等信息,并在百度地图上以点、线和图表的形式显示出车辆的坐标和运行轨迹等;测试结果表明,该系统运行稳定,定位精度高,获取数据准确、时延小,能够满足应用需求。     

多总线通讯服务器设计与实现

为了解决大当量战斗部爆炸威力试验中测试参数多、测试区域广,多设备统一触发的难题,文中提出了分布式无线网络化的测试思路,进行了无线传输系统框架设计,简要介绍了系统的组成、功能、结构设计、各个通信点位的设计、网络构成、工作频率、电源系统设计等,使大家对该测试系统有一个全面的认识。设计人员研发了相关硬件设备,并进行了多发试验验证。该测试系统组网方式灵活,传输速率高,具备GPS同步和无线触发功能,可进行远程操控和数据下载,使用方便,可靠性高,提高了大当量战斗部爆炸威力的测试效率。