移动通信延迟对信息并行传输效率影响仿真

通信信息并行传输效率是衡量移动通信网络系统性能的重要指标。为了提高移动网络信息通信速率,研究通信延迟对信息并行传输的影响。根据移动网络通信理论和IEEE802.11协议对移动网络信息并行通信过程进行分析,构建移动通信网络模型,得到串行延时、"数据包"的串行延时、路由延时、传播延时等参数,获取移动通信延迟与信息并行传输效率之间的约束关系,分析各个延迟参数对信息并行通信速率的影响。仿真结果表明,随着通信延迟的不断变化,信息并行传输速率也在发生变化,因此降低通信延迟能最大程度地提高信息并行传输速率。     

基于ARIMA和BP神经网络的生态系统服务价值预测仿真

针对现有生态系统服务价值预测体系误差大、研究不足等问题,本文构建了面向未来生态服务价值的预测机制。该机制采用主成分分析法和Person相关系数法,建立了以GDP、人口、土地利用率、土地管理政策和城市人口密度为驱动力的生态系统服务价值关联矩阵,提出了基于ARIMA和BP神经网络的生态系统服务价值的时间序列预测机制。为验证预测机制有效性,本文选取中国主要土地利用类型代表省份的真实土地数据集进行分析,研究结果表明本文建立的预测模型平均绝对误差仅为0.023,且从预测结果来看,未来草地生态会向较好趋势发展,林地生态发展不容乐观。     

基于信息熵与形态学的鸟鸣数据处理

为了探索一种减少训练量并提高精度,且适应于卷积神经网络的预处理方法,以识别鸟鸣为例,基于信息熵以及形态学在图像处理上的应用,根据鸟鸣的间隔性特点,提出新的处理方案。利用形态学将音频片段归类为有效和噪声信号两类;使用加权的方法利用信息熵预测有效数据的分布。仿真结果表明,形态学使数据量减少且信息熵处理使单个数据稀疏化并起到滤波作用,在保持精度甚至精度提高的情况下缩短了训练时间,为卷积神经网络的数据预处理的简化提供了方向。     

Supply chain network design under the risk of uncertain disruptions

Facility disruptions in the supply chain often lead to catastrophic consequences, although they occur rarely. The low frequency and non-repeatability of disruptive events also make it impossible to estimate the disruption probability accurately. Therefore, we construct an uncertain programming model to design the three-echelon supply chain network with the disruption risk, in which disruptions are considered as uncertain events. Under the constraint of satisfying customer demands, the model optimises the selection of retailers with uncertain disruptions and the assignment of customers and retailers, in order to minimise the expected total cost of network design. In addition, we simplify the proposed model by analysing its properties and further linearise the simplified model. A Lagrangian relaxation algorithm for the linearised model and a genetic algorithm for the simplified model are developed to solve medium-scale problems and large-scale problems, respectively. Finally, we illustrate the effectiveness of proposed models and algorithms through several numerical examples.     

International Trade

Values of net oil imports(-)/exports for Austria, Belgium, Denmark, Finland, France, Germany, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, Japan, Canada, the United States and Australia. Updated on a monthly basis. Number of imports for Belgium, France, Germany, Greece, Republic of Ireland, Italy, Netherlands, Portugal, Spain, Sweden, the United Kingdom, and Other EU-15 (EU-15), Norway, Poland, Switzerland, Turkey, and Other Europe (OECD Europe), Canada, Chile, Mexico, and the United States (OECD Western Hemisphere), and Australia, Japan, Republic of Korea, New Zealand, (OECD Asia-Pacific), and Total OECED. Current data for Austria, Belgium, Finland, France, Germany, Greece, Italy, Netherlands, Portugal, Spain, Sweden, the United Kingdom, Other EU-15 (EU-15), Czech Republic, Hungary, Poland, Slovakia, Turkey, and Other Europe (OECD Europe), Canada, Mexico, and the United States (OECD Western Hemisphere), Australia, Japan, Republic of Korea, and New Zealand (OECD Asia-Pacific). Updated on a monthly basis. Current data for principal importers of natural gas and the amount in which they import from United States, Republic of Korea, United Kingdom, France, Japan, Italy, Germany and Spain (Table 19.1). Current data for principal exporters of natural gas and the amount they export to Canada, Trinidad and Tobago, the United States, Netherlands, Norway, Russia, Turkmenistan, Qatar, Algeria, Nigeria, Indonesia, Malaysia and Australia (Table 19.2). Updated on a monthly basis.     

基于BERT-Att-biLSTM模型的医学信息分类研究

目前,对互联网上虚假健康信息的研究多集中于谣言识别,而对医学信息自动分类的研究较少。采用基于双向编码的语言表征模型和注意力增强的双向长短时记忆模型(BERT-Att-BiLSTM模型),对健康信息文本进行分类,实现自动识别虚假健康信息。实验结果表明,BERT-Att-BiLSTM模型可以高效地对医学信息进行分类,其中BERT模型相较于BiLSTM模型,性能提升明显;与融合Word2Vec的BiLSTM模型相比,BERT-Att-BiLSTM模型效果更佳。     

Sensei: Enforcing secure coding guidelines in the integrated development environment

We discuss the potential benefits, requirements, and implementation challenges of a security-by-design approach in which an integrated development environment plugin assists software developers to write code that complies with secure coding guidelines. We discuss how such a plugin can enable a company's policy-setting security experts and developers to pass their knowledge on to each other more efficiently, and to let developers more effectively put that knowledge into practice. This is achieved by letting the team members develop customized rule sets that formalize coding guidelines and by letting the plugin check the compliance of code being written to those rule sets in real time, similar to an as-you-type spell checker. Upon detected violations, the plugin suggests options to quickly fix them and offers additional information for the developer. We share our experience with proof-of-concept designs and implementations rolled out in multiple companies, and present some future research and development directions.     

基于工程教育专业认证的数字电子技术课程的改革

工程教育专业认证是高校走向国际化的必经之路,数字电子技术课程是一门应用类型的电气信息类基础课,也是近十几年来发展最快的技术。结合工程教育专业认证对电子信息类专业本科毕业生的十二条通用要求,结合现代教学方法,促进理论与实践融合,在课程理论教学内容的组织方面、实践项目设计方面、教学方法上进行了改革。实践证明,改革提高了学生学习的趣味性、锻炼培养了学生的工程技能和素养。     

Convolutional neural network with adaptive inferential framework for skeleton-based action recognition

In the task of skeleton-based action recognition, CNN-based methods represent the skeleton data as a pseudo image for processing. However, it still remains as a critical issue of how to construct the pseudo image to model the spatial dependencies of the skeletal data. To address this issue, we propose a novel convolutional neural network with adaptive inferential framework (AIF-CNN) to exploit the dependencies among the skeleton joints. We particularly investigate several initialization strategies to make the AIF effective with each strategy introducing the different prior knowledge. Extensive experiments on the dataset of NTU RGB+D and Kinetics-Skeleton demonstrate that the performance is improved significantly by integrating the different prior information. The source code is available at: https://github.com/hhe-distance/AIF-CNN.     

New doctors ranking system based on VIKOR method

Nowadays, we can use different websites that help us make decisions about various aspects of our lives. However, privacy protection prevents websites from providing personalised guidelines to users. We propose a novel doctor‐ranking system (DRS) based on multi‐criteria group decision‐making (MCGDM) method to address the problems of privacy protection. The following aspects differentiate our proposed DRS model from previous works: (a) textual information reviews are used to identify user preferences and complementary criteria, (b) criteria weights are determined by term frequency inverse document frequency (TF‐IDF) instead of Delphi method or expert opinion, (c) intuitionistic fuzzy sets (IFSs) are used to replace sentiment analysis to express subjective user criteria, and (d) VIsekriterijumsko KOmpromisno Rangiranjie (VIKOR) method for MCGDM with IFSs is used to solve the doctor‐ranking problem. We apply our proposed model to datasets from Haodf.com to compare the performance of our method with that of sentiment analysis and technique for order performance by similarity to ideal solution (TOPSIS) methods. The experimental results show that our method provides accurate ranking and increases the reliability of DRS.