通用数据采集板的设计与实现

本采集板主要是针对单片机而设计的位或位通用采集系统。可以实现单端路或双端路摸拟1283216量输入,输入电压范围,0 ~ 5V,5V。采集板使用光电隔离技术,具有较强的抗干扰性能。10V     

企业数据库体系化环境及建设

信息化建设是目前企业为提高自身竞争而正在做的一项重要工作，企业信息化的目的就是要让自己和服务对象能及时获得所需要的信息，以调整和控制企业的运营，为用户提供更好的产品和服务，章从企业数据环境的角度，给出了企业数据库体系化环境的概念，说明了各层次数据库的作用，探讨了数据库体系化环境的建设。     

数据挖掘技术在客户关系管理中的应用研究

客户关系管理以客户为中心,深入研究客户行为,通过客户关怀提高客户满意度,进而提高企业的竞争力。而数据挖掘技术则为客户关系管理提供了强有力的技术支持。文章阐述了数据挖掘技术在客户关系管理中应用的研究背景和意义,并介绍了客户关系管理思想、数据挖掘的过程和方法、以及数据挖掘技术在客户关系管理中的应用。     

An empirical investigation of factors influencing the adoption of data mining tools

Previous studies explored the adoption of various information technologies. However, there is little empirical research on factors influencing the adoption of data mining tools (DMTs), particularly at an individual level. This study investigates how users perceive and adopt DMTs to broaden practical knowledge for the business intelligence community. First, this study develops a theoretical model based on the Technology Acceptance Model 3, and then examines its perceived usefulness, perceived ease of use, and its ability to explain users’ intentions to use DMTs. The model's determinants include 4 categories: the task-oriented dimension (job relevance, output quality, result demonstrability, response time, and format), control beliefs (computer self-efficacy and perceptions of external control), emotion (computer anxiety), and intrinsic motivation (computer playfulness). This study also surveys the moderating effect of experience and output quality on the determinants of DMT adoption and use. An empirical study involving 206 DMT users was conducted to evaluate the model using structural equation modeling. Results demonstrate that the proposed model explains 58% of the variance. The findings of this study have interesting implications with respect to DMT adoption, both for researchers and practitioners.     

异构数据库数据迁移研究

数据迁移是指将一种数据环境中的数据根据需要进行某种转换,导入另外一种相异或者相同的数据环境中。异构数据库迁移是指将数据库管理系统中不同的,或者数据结构、数据组成与定义相异的数据之间尽量进行等价、无损的数据转移。通过文献案例和理论研究对异构数据库的迁移技术进行了分析,提出了一种以Java为编程语言和数据库结构JDBC为基础的异构数据库管理系统的迁移方法,并通过实验证实了此方法的有效性。     

Web数据挖掘在急性高原病易感人群预测中的应用

将Web数据挖掘技术应用于急性高原病易感人群的预测中,通过Web技术与数据挖掘技术相结合,将分散于Web中的数据经过检索、清洗、处理后存储在专门的数据仓库中。在现有研究成果的基础上,提出一个简便、高效的急性高原病易感人群预测模型。     

高职《数据结构》教学改革与探索

“数据结构”是计算机专业中一门重要的专业基础课程,同时又是计算机专业中一门难教和难学的课程。本文针对高职生源的实际情况,探索对该课程的教材、教学内容、教学方法、实验等方面进行改革,以提高该门课的教学效果。     

城市规划专题信息数据标准化研究

从现有城市规划数据的数据分类、数据代码、数据标准、数据报告入手,结合城市用地国家分类标准进行分析,提出一种包括专题规划信息分类、数据编码、数据分层的规划专题信息数据标准化技术。     

Artificial intelligence implication on energy sustainability in Internet of Things: A survey

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges.     

Unravelling the drivers of technological innovations in the Czech Republic: Do international technological linkages matter?

This study aims to investigate the main factors driving technological innovation within firms in the manufacturing and service sectors of the Czech Republic. We apply a binary logistic regression model to cross-sectional data from 502 firms, obtained from the World Bank Enterprise Survey. The results of our empirical investigation show that certain elements of the business environment, such as the tax rate, serve as significant obstacles to firms’ product innovations. The results also confirm that international technological linkages—measured by international quality certificates and foreign technology licenses—affect technological innovations. Moreover, we found that internal R&D activities positively impact technological innovation across all sectors; contrarily, we found that process innovation in the manufacturing sector is positively influenced by foreign technology licenses and business association membership. Process innovations in the service sector are positively correlated with external R&D and financing from banking institutions. Finally, business association membership does not positively influence technological innovation in the service sector. Our findings have salient implications for firm managers, policymakers, and scholars aiming to explore and improve innovation outcomes in transitional economies.