基于射频技术的无线识别系统设计

本无线识别系统的设计利用射频技术,阅读器将呼叫信号加载到频率为70MHZ的射频上,以无线方式发给应答器。呼叫信号经过线圈耦合、解调、电压比较器等电路传送到应答器的控制器。控制器对呼叫信号进行分析、确认后,将存储的四位二进制编码通过发送端的调制电路馈送到阅读器,并显示应答器的具体信息。     

谈受限汉语在机器翻译中的运用——以中文简历英译为例

随着国际交流的深入,越来越多的中国人需要把中文简历翻译成英文。但是,简历汉语的特殊性使机器翻译还存有许多不足,导致汉译英的简历表达不尽如人意。要解决这一问题,就有必要对简历中汉语的词汇、句法和语义等进行限制,并建立翻译句模,以简化句子结构,减少或消除汉语表达的歧义,从而提高机器翻译系统的译文质量。受限汉语和句模在机器翻译中的应用,可以推广到饭店订餐和房间预订等受限领域的机器翻译系统中。     

机器学习视角下中国去产能产业的识别

随着中国环境压力的增加,政府提出了供给侧改革,去产能是改革的主要内容,但是由于产业特征的实时演变,需要对政策进行完善。文章运用模糊C均值算法和支持向量机算法分析现阶段需要进行去产能的产业,结果发现在现行去产能政策中大部分行业是需要去产能的,但煤炭开采和洗选业以及铁路、船舶、航空航天和其他运输设备制造业已不适合继续去产能,同时将化学原料和化学制品制造业加入去产能行列中。     

吊挂式高楼清洗机

安全可靠的高楼清洗设备设计已成为当前重要课题。由于高空作业要求安全、稳定、贴墙、行走、平衡等,文章突破吊栏等传统结构,采用双钢丝吊挂式设计,从材料选用到自带电、液结构等方面,对高楼清洗机进行了优化设计,保证高楼清洗机的安全、轻便与简洁。     

Machine Learning for Drug Overdose Surveillance

ABSTRACT

We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan (Herlands, McFowland, Wilson, & Neill, 2017 Neill, D. B. (2017). Multidimensional tensor scan for drug overdose surveillance. Journal of Public Health Informatics, 9(1), e20. doi:10.5210/ojphi.v9i1.7598[Crossref] , [Google Scholar]) enables early detection of emerging patterns in spatio-temporal data, accounting for both the complex, correlated nature of the data and the fact that detecting subtle patterns requires integration of information across multiple spatial areas and multiple time steps. We apply this approach to 17 years of county-aggregated data for monthly opioid overdose deaths in the New York City metropolitan area, showing clear advantages in the utility of discovered patterns as compared to typical anomaly detection approaches. To detect and characterize emerging overdose patterns that differentially affect a subpopulation of the data, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved), we apply the Multidimensional Tensor Scan (Neill, 2017 Neill, D. B. (2017). Multidimensional tensor scan for drug overdose surveillance. Journal of Public Health Informatics, 9(1), e20. doi:10.5210/ojphi.v9i1.7598[Crossref] , [Google Scholar]) to 8 years of case-level overdose data from Allegheny County, Pennsylvania. We discover previously unidentified overdose patterns which reveal unusual demographic clusters, show impacts of drug legislation, and demonstrate potential for early detection and targeted intervention. These approaches to early detection of overdose patterns can inform prevention and response efforts, as well as understanding the effects of policy changes.     

人学视野下的当代教育与教育的人学取向

中国现行教育在很大程度上仍然存在人学缺失的现象。这显然与时代潮流相违背。要纠正这一偏差,必须从人学视野审视教育,同时提倡教育的人学取向。人学视野下的教育旨在确立以人为本的教育理念,教育的人学取向则最终应该落脚在教育如何促进人的全面发展这一终极目标上。     

基于SVM的Web日志挖掘及潜在客户发现

潜在的客户资源是商家未来的利润来源,发现了潜在的客户就可以制定相应的商业决策,并进行有针对性的客户关系管理。使用SVM方法对Web日志文件进行挖掘,以发现站点访问者中潜在客户的共同行为模式,并将其分为不同级别的目标客户群。同时,通过试验4种不同比例的训练样本,研究了非对称数据对分类结果的影响,以期获得较优的模型。     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Toward Probabilistic Prediction of Flash Flood Human Impacts

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance‐specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle‐related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time‐ and space‐dependent human vulnerability and risk assessment for short‐fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact‐based predictive models for flash flood casualties using machine‐learning approaches in the future.