基于RFID技术的健康证管理系统开发及应用

开发了一种基于RFID技术的健康证管理系统。该系统采用PHILIPS公司的Mifare读写卡作为射频识别解决方案,以89c52单片机作为读写控制芯片,使用手持式POS机作为移动读取设备。在健康证管理过程中实现了安全、高效、移动、非接触的健康信息确认。     

SVM决策树在沉积微相识别中的应用

支持向量机(SVM)作为统计学理论最年轻的分支,其应用日益广泛。针对油层沉积微相的多类识别问题,可采用支持向量机和决策树相结合的方法。对传统的SVM决策树进行改进的基础上,在SVM核函数选取过程中,构造了与实际问题有关的核函数。此方法有效的降低了支持向量机的设计难度,同时提高了识别精度和泛化能力。最后用实例对比神经网络验证了该方法的优越性。     

The impact of deep learning on document classification using semantically rich representations

This paper presents a semantically rich document representation model for automatically classifying financial documents into predefined categories utilizing deep learning. The model architecture consists of two main modules including document representation and document classification. In the first module, a document is enriched with semantics using background knowledge provided by an ontology and through the acquisition of its relevant terminology. Acquisition of terminology integrated to the ontology extends the capabilities of semantically rich document representations with an in depth-coverage of concepts, thereby capturing the whole conceptualization involved in documents. Semantically rich representations obtained from the first module will serve as input to the document classification module which aims at finding the most appropriate category for that document through deep learning. Three different deep learning networks each belonging to a different category of machine learning techniques for ontological document classification using a real-life ontology are used.Multiple simulations are carried out with various deep neural networks configurations, and our findings reveal that a three hidden layer feedforward network with 1024 neurons obtain the highest document classification performance on the INFUSE dataset. The performance in terms of F1 score is further increased by almost five percentage points to 78.10% for the same network configuration when the relevant terminology integrated to the ontology is applied to enrich document representation. Furthermore, we conducted a comparative performance evaluation using various state-of-the-art document representation approaches and classification techniques including shallow and conventional machine learning classifiers.     

Artificial intelligence for decision making in the era of Big Data – evolution,challenges and research agenda

Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.     

Guest reviewers

What if new cars could adjust in-car devices to particular ways of speaking around the world? General Motors sought communication research to inform the design of culturally competent in-car human–machine interfaces. We discuss concerns and tensions that arise when balancing commitments of naturalistic study with design development goals. We emphasize the importance of awareness, attentiveness, and cooperation to ensure research integrity.     

浅谈支持向量机技术

支持向量机是基于统计学习理论的一种新兴的机器学习技术。本文论述了支持向量机技术的研究和目前的应用状况,并指出了支持向量机技术在应用研究中的一些待解决问题和研究方向。     

《茶业机械》课程教学改革与实践

《茶业机械》作为茶学专业的专业核心课,内容涉及与茶业有关的机械设备的构造、原理及使用技术,是机械工程学与茶树栽培学、茶叶加工学相结合的一门应用学科。为建设符合茶学专业人才需求发展的茶业机械课程体系与教学方法,全面提高茶学本科学生的业务素质和创新能力,积极探索了课程的教学改革与实践。从茶业机械的课程体系、教学内容、教学方法、实践教学、创新能力培养等方面进行了探索与实践,为培养具有较强实践操作能力,创新能力的“复合型”茶学人才寻找新模式,提高教学效果和教学质量。     

基于相关向量机的大豆生长发育阶段预测

精确地预测大豆的生长发育阶段对于农业生产和研究都具有重要的意义,采用相关向量机方法为大豆的生长发育阶段预测建模,通过优化建模参数,所建相关向量机模型具有较高的拟合能力,且预测误差小,稳健性好。实验结果表明,相关向量机建立的模型优于神经网络。     

基于机器视觉技术的钨矿初选工艺研究

本文首先分析了机器视觉技术应用在钨矿初选环节的可行性,其次对钨矿初选系统实现、工艺算法分析进行了详细介绍,最后通过钨矿筛选的具体算法分析表明机器视觉技术在钨矿初选环节使用的可行性、高效性。     

基于UAMRT的英汉翻译系统的设计与实现原理

本文提出了一种设计和实现英汉翻译系统的新方法。该方法以模板匹配替换通用算法 (UAMRT)为基础 ,通过结合句型分析算法和从句分析算法建立启发式搜索机制 ,实现从源语言句子到目标语言句子的自动翻译。它不仅能够合理地利用基于规则和基于例子的翻译系统的优点 ,而且能够在一定程度上克服它们的缺点。速度测试表明以该方法为基础实现的英汉翻译系统在P IV1 7G的计算机上的翻译速度每秒可以达到 130 0个单词左右 ;质量测试表明该系统具有较好的发展潜力