通关单柜员式自助打印系统的设计与实现

本研究成功研制的通关单自助打印系统,针对通关单柜台人工打印工作量大、费时耗力等问题,集成应用了真空吸纸控制、走纸直通道、快速升降台与大容量进纸容器、激光对射厚度检测、位移传感器检测、定制开发PR2打印机设备等多项光机电先进技术,设计实现具有纸张调度、OCR识别、打印控制3个子系统的打印装置,有效解决一式多联并可能带有附页的通关单安全、快速、批量打印问题;建立了与CIQ2000系统无缝衔接的应用系统,实现自动下载通关单数据,自助打印通关单,自动发送电子通关单到海关,自动核销通关单流水号等功能。该系统的应用提高了通关效率,提升了通关单签证质量,减轻了检务前台工作压力,有效缓解了人员不足的矛盾,为建设和扩展检验检疫自助报检大厅功能,为企业提供远程延伸服务奠定了基础。     

我国风力发电项目投资风险评价——基于支持向量机模型

在分析我国风力发电项目投资风险并建立其评价指标体系的基础上,将支持向量机模型用于风力发电项目投资风险评价,全面衡量风力发电项目面临的经济风险、技术风险、政策风险等。支持向量机方法很好地解决了风力发电项目样本数据缺失、评价主观性问题。实证分析结果表明,利用支持向量机模型评价风力发电项目的投资风险,具有一定的适用性和准确性。     

机器类设备基于不同时延需求分组的随机接入方法

针对大规模机器类通信中拥塞导致的时延敏感设备时延高和接入成功率低的问题,提出将小区中设备按时延要求分组,对不同组设备引入不同的退避模型,分析时延敏感设备的时延和吞吐量,按照不同组中设备的时延需求动态分配前导数目,同时通过调整接入类限制因子实现吞吐量的优化。仿真结果表明,在给定时延敏感设备的时延限制条件时,与统一退避的机制对比,所提分组机制的时延敏感设备能够满足时延要求,并且提高了接入效率。     

沥青混凝土边模铺装机的安装系统设计

为了提高路面施工的效率和质量,采用边模安装技术,设计了一款沥青混凝土边模铺装机,并提出了安装系统的总体设计方案。边模安装系统包括边模出仓装置、自动挂钩装置和射钉装置。边模出仓装置由定位卡、出仓斜坡滑道、推块和气缸组成;自动挂钩装置主要由弹夹装置、推动装置、挂钩装置和连杆结构组成;射钉装置由气钉枪、定位模块和三坐标工作台组成。采用UG软件对边模安装系统各部分结构进行三维建模,并虚拟装配,检验了设计的合理性。结果表明,沥青混凝土边模铺装机的行走系统可按照施工的方向自动行走,边模铺装系统可以实现边模的自动铺设,提高了铺设道路的质量;边模安装机工作方式相比于传统人工铺设方式,具有结构可靠、效率高、操作简单、安全性能好、污染少等优点。研究成果可为未来全智能化铺设边模设备优化提供参考,并能结合沥青摊铺机应用于施工中,进一步提高铺装质量。     

Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

iSDS: a self-configurable software-defined storage system for enterprise

ABSTRACT

Storage is one of the most important aspects of IT infrastructure for various enterprises. But, enterprises are interested in more than just data storage; they are interested in such things as more reliable data protection, higher performance and reduced resource consumption. Traditional enterprise-grade storage satisfies these requirements at high cost. It is because traditional enterprise-grade storage is usually designed and constructed by customised field-programmable gate array to achieve high-end functionality. However, in this ever-changing environment, enterprises request storage with more flexible deployment and at lower cost. Moreover, the rise of new application fields, such as social media, big data, video streaming service etc., makes operational tasks for administrators more complex. In this article, a new storage system called intelligent software-defined storage (iSDS), based on software-defined storage, is described. More specifically, this approach advocates using software to replace features provided by traditional customised chips. To alleviate the management burden, it also advocates applying machine learning to automatically configure storage to meet dynamic requirements of workloads running on storage. This article focuses on the analysis feature of iSDS cluster by detailing its architecture and design.     

The impact of German-speaking online media on tourist arrivals in popular tourist destinations for Europeans

This empirical study analyzes the relationship between the sentiments in online media with regard to travel destinations and corresponding tourist arrivals. We expect the media reports on political and economic instability and turmoil to enhance tourist arrival nowcasts and forecasts, as they can probably complement them with information on disruptions and shocks. Therefore, we believe this research will help to build better models for tourism demand nowcasting and forecasting. We use the sentiment in the German-speaking online media because the German-speaking region is the most populated in Europe and has the largest group of travelers visiting destinations in and around Europe.

An artificial neural network is used to analyze the mood of the media. The software classifies news items regarding potential tourist destinations with either positive or negative labels. The number of positive and negative news items is used to build sentiment indices for popular tourist destinations for Europeans.

Our results show strong correlations between the mood concerning tourist destinations and tourist arrivals in these countries. Indeed, disruptions and shocks prevalent in the news are reflected in similar ratios in both tourist arrivals and sentiment indices. These results can be used as a new explanatory variable for tourism demand modelling.     

Bayesian forecasting of electoral outcomes with new parties’ competition

We propose a new methodology for predicting electoral results that combines a fundamental model and national polls within an evidence synthesis framework. Although novel, the methodology builds upon basic statistical structures, largely modern analysis of variance type models, and it is carried out in open-source software. The methodology is motivated by the specific challenges of forecasting elections with the participation of new political parties, which is becoming increasingly common in the post-2008 European panorama. Our methodology is also particularly useful for the allocation of parliamentary seats, since the vast majority of available opinion polls predict at national level whereas seats are allocated at local level. We illustrate the advantages of our approach relative to recent competing approaches using the 2015 Spanish Congressional Election. In general, the predictions of our model outperform the alternative specifications, including hybrid models that combine fundamental and polls models. Our forecasts are, in relative terms, particularly accurate in predicting the seats obtained by each political party.     

Growth machines and social movements in mature tourist destinations Costa del Sol-Málaga

Abstract

The purpose of this study is to analyse the new processes of tourism growth and its conflicts from the perspective of social movements. First, the urban growth machine analysis model is applied by the systematisation of six projects. Second, the resistance movements against those projects and whether this resistance could be the start of local tourism degrowth policies are examined. The methodology is qualitative, based on documentary analysis, participatory observation, discussion groups and interviews. The case study is the destination of Costa del Sol-Málaga. The results enable the development of the urban growth machine model in tourist destinations. Meanwhile, social movements demystify the argument based on neoclassical economic progress. The social movements condemn the effects of large-scale top-down projects, and implement alternative bottom-up proposals. Although the social movements do not reject tourism, they call for greater control over its impact, denounce unlimited growth, overtourism and the loss of urban quality of life. These movements advocate a lifestyle linked to the everyday space, which they believe is threatened by excessive urban-tourism growth. They are a symptom of the need to devise a proposal using the principles of degrowth.