基于GIS的农产品指标数据库表结构设计研究

为了将GIS(地理信息系统)有效地应用到农产品统计中,阐述了农产品指标的3种维度——属性维度、时间维度、空间维度。分析了全关系数据库模型、面向对象数据库模型、对象关系混合数据库模型对GIS数据存储的各类特点,考虑到农产品指标数据本身的特点,提出了采用关系型数据库存储GIS数据与农产品指标数据,并以此模型为基础,设计和分析了数据库物理设计中农产品指标横向与纵向存储的两种结构方案。通过测试数据验证了两者的优缺点,并提出了数据库分区和面向时间或空间维度独立建表的优化途径。     

某型装甲车制动器热-应力-磨损耦合仿真分析

为提高鼓式制动器在热-应力耦合影响下磨损计算的准确性,考虑实际制动过程中温度升高对制动器性能的影响,通过增加温度与磨损率的变化关系修正传统Archard磨损模型,对某型装甲车鼓式制动器进行仿真计算.分析结果表明,在热-应力有限元迭代求解过程中,采用完全耦合法持续更新制动器磨损面的几何形状可以不断地修正热-应力的计算结果,因此能更真实地描述制动过程中热-应力-磨损的复杂耦合现象.仿真得到制动器在4种典型工况下的磨损规律,为制动器的优化设计提供依据.     

从软件工程的发展看软件自动化*

本文对软件工程方法学发展各个阶段主要的方法论做了简单的评述，并展望了软件工程今后发展的方向，同时举例说明了以此为指导思想进行软件自动构造的方法．     

CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention

Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.     

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

N6-methyladenine (6mA) has been recognized as a key epigenetic alteration that affects a variety of biological activities. Precise prediction of 6mA modification sites is essential for understanding the logical consistency of biological activity. There are various experimental methods for identifying 6mA modification sites, but in silico prediction has emerged as a potential option due to the very high cost and labor-intensive nature of experimental procedures. Taking this into consideration, developing an efficient and accurate model for identifying N6-methyladenine is one of the top objectives in the field of bioinformatics. Therefore, we have created an in silico model for the classification of 6mA modifications in plant genomes. ENet-6mA uses three encoding methods, including one-hot, nucleotide chemical properties (NCP), and electron–ion interaction potential (EIIP), which are concatenated and fed as input to ElasticNet for feature reduction, and then the optimized features are given directly to the neural network to get classified. We used a benchmark dataset of rice for five-fold cross-validation testing and three other datasets from plant genomes for cross-species testing purposes. The results show that the model can predict the N6-methyladenine sites very well, even cross-species. Additionally, we separated the datasets into different ratios and calculated the performance using the area under the precision–recall curve (AUPRC), achieving 0.81, 0.79, and 0.50 with 1:10 (positive:negative) samples for F. vesca, R. chinensis, and A. thaliana, respectively.     

再谈电容器真空浸渍"单台注油"装置

介绍了电容器单台注油装置的构成及工作原理,对关键零部件作了改进,对其作用进行了分析,并说明如何安装使用。     

Tuning space mapping with tuning exponent of T‐matrix

Tuning space mapping (TSM) with tuning exponent parameter of T‐matrix is proposed. A section of design interest in the electromagnetic (EM) model is replaced by “n” pieces cascaded T‐matrixes, and each T‐matrix is the EM‐simulated T‐parameter of preassigned unit cell circuit (PUCC). Finally the optimal exponent parameter of T‐matrix is transferred to original design variables. The proposed TSM not only inherits the advantages of circuit tuning element‐less (CTEL) TSM in prior art but also overcome the number range limitation that the tuning parameter must be positive for tuning in CTEL TSM. The proposed method has the minimal specification error and lowest simulation time comparing with other TSM methods. Verification examples, comparisons and discussions are also implemented. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:232–239, 2016.     

一种足球机器人中指定颜色属性物体的识别方法

为解决足球机器人中已知颜色属性的物体的识别问题,本文提出了一种基于HSV模型，应用H、V参数特征值来识别指定颜色属性物体的方法，在HSV模型中，参数H能较集中的反映被识别物体的颜色种类特性，但当RGB值较小时，色调趋向于不确定，可以通过引入参数V改进这点不足，从而有效的实现颜色物体的识别。     

基于爬虫的数据监控系统

近年来,随着互联网技术的快速发展,云计算服务开始全面普及,大型互联网公司以及中小型企业已经开始提供自己的云计算服务,中国科学院也提供了云计算服务平台——中科院“科技云”.本文针对中国科学院“科技云”项目的实际需求,参考已有商业云监控系统的功能和运行模式,设计并实现了一种基于爬虫的数据监控系统.该系统相比商业云监控系统,支持URL（布尔）类型数据监测的同时,增加了对数值类型、文本类型的监测,更好的支持第三方服务的监测,并实现了服务故障警报,监测数据可视化以及监测数据分布式存储.     

一种高效Cable Modem SOC软硬件协同验证平台

通过对基于龙芯一号CPU的双向有线调制解调器SOC的研究,利用软件仿真和硬件模拟相结合的方法,设计了一种软硬件协同验证平台TWCNP,可分别对EuroDOCSIS协议栈、EuroDOCSIS协议处理器、整个双向有线网络SOC等软硬件进行验证,并实现了验证和设计的统一.为了提高TWCNP的验证时效性,提出了一种基于TWCNP-OS的软硬件协同验证方法,缩短了开发周期,保证了SOC设计的可靠性.