计算化学网格的研究与设计

提出一种计算化学网格的体系结构,从资源管理、作业管理和调度等方面对该体系结构进行了分析和设计.在实验室局域网环境下,采用Globus Toolkit 4.0(GT4)作为网格中间件,构建此计算化学网格.计算化学网格的设计和实现,不仅可以使得实验室内的计算资源得到充分的利用,扩大一些昂贵且不可复制资源的用户群,还能够消除资源孤岛和信息孤岛,为用户提供一个安全、可靠并可扩展的Web应用环境.     

基于蓝牙4.0的教室人数监测智能系统设计

为了能实时监测教室人数,方便各大高校的日常教学及管理活动,提出了一种基于蓝牙4.0技术、用于统计教室人数的智能系统。本系统以基于蓝牙低功耗技术的 CC2540射频单片机为核心,通过控制红外光电传感器和温湿度传感器进行数据的采集,并通过 WiFi 模块将采集到的数据传输给远程上位机、手机等终端设备,也可以通过 WiFi 模块查询教室的信息。实际测试结果表明,该设计可靠性高、功耗低、准确度高,可广泛应用于各大高校的日常教学活动中。     

Smart Innovation Engineering: Toward Intelligent Industries of the Future

ABSTRACT

Knowledge-based engineering systems are founded upon integration of knowledge into computer systems and are one of the core requirements for the future Industry 4.0. This paper presents a system called smart innovation engineering (SIE) capable of facilitating product innovation process semi-automatically. It enhances decision-making processes using the explicit knowledge of formal decision events. The SIE system carries the promise to support the innovation processes of manufactured products in a quick and efficient way. It stores and reuses past decisional events or sets of experiences related to innovation issues, which significantly enhances innovation progression. The analysis of basic concepts and implementation method proves that SIE system is an advanced form of cyber physical systems. It is flexible, systematic, fast, and supports customization. It can play a vital role toward Industry 4.0 development.     

"服装教学"远程系统的开发与应用

阐述了基于Web多媒体网络,集教、学、管、设计于一体的"服装教学"远程系统的设计思路、系统结构、逻辑框架、开发工具及系统开发人员的确定,开发出了"服装教学"远程教学系统,实际应用效果良好.     

基于BLE4.0姿态解算的无线语音鼠标设计

设计了一款残疾人专用智能无线语音鼠标,让双手不灵活的残疾人灵活地操纵电脑成为现实.本设计发送端采用BLE4.0技术负责头部姿态解算、语音识别、向电脑发送操作指令等功能.无需任何接收装置,只需与电脑自带的蓝牙设备配对后便可通过相应的蓝牙协议对电脑直接进行操控.     

Artificial neural network-driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

In recent years, smart healthcare, artificial intelligence (AI)-aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL-based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre-established datasets that fail to capture real-life applicability. Therefore, this study proposes an AI-enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare-based wearable devices (WD) for real-time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL-based architecture aggregates the optimizer weights of many clients using a fifth-generation (5G) communication channel. Then, the performance of the proposed FL-based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.     

SCSI接口技术及硬盘驱动器

随着企业自动化、信息化投入程度的加强,本文针对计算机应用的发展趋势介绍SCSI接口的各种情况。     

Human factors and ergonomics in manufacturing in the industry 4.0 context – A scoping review

Industry 4.0 revolution has brought rapid technological growth and development in manufacturing industries. Technological development enables efficient manufacturing processes and brings changes in human work, which may cause new threats to employee well-being and challenge their existing skills and knowledge. Human factors and ergonomics (HF/E) is a scientific discipline to optimize simultaneously overall system performance and human well-being in different work contexts. The aim of this scoping review is to describe the state-of-the-art of the HF/E research related to the industry 4.0 context in manufacturing. A systematic search found 336 research articles, of which 37 were analysed utilizing a human-centric work system framework presented in the HF/E literature. Challenges related to technological development were analysed in micro- and macroergonomics work system frameworks. Based on the review we frame characteristics of an organisation level maturity model to optimize overall sociotechnical work system performance in the context of rapid technological development in manufacturing industries.     

Scientific mapping to identify competencies required by industry 4.0

The objective of this study was to identify what competencies are identified in the literature as necessary for Industry 4.0 by conducting a survey of the literature and a scientific mapping of the evolution of the issues related to the qualification of professionals for Industry 4.0 and possible paths for research and education. A search was conducted on the Scopus, Web of Science and Science Direct databases for the interval from 2010 to 2018. This systematic review revealed topics and authors currently specialized in the field and allowed mapping the field of study. The identification of journals and keywords useful in future studies was also an object of this study. SciMAT software was used for the systematic literature review. The results are highlighted by the set of competencies (knowledge and skills) that must be developed in professional education to accompany the new industrial revolution, as well as the importance of integrating efforts by companies, governments and universities. These efforts should focus on creating “learning factories”, which are understood to be environments that provide practical experiences to these professionals, preparing them in the best way possible for the requirements of Industry 4.0. This conceptual map showed that the main competencies needed include skills: (leadership, strategic vision of knowledge, self-organization, giving and receiving feedback, pro-activity, creativity, problem solving, interdisciplinarity, teamwork, collaborative work, initiative, communication, innovation, adaptability, flexibility and self-management) and knowledge of contemporary fields (information and communication technology, algorithms, automation, software development and security, data analysis, general systems theory and sustainable development theory).     

Development of deformation model for simulation of mechanical behaviors of coated-cladding

A coated fuel cladding is one of the promising accident tolerant fuel (ATF) cladding concepts to enhance the fuel safety for accident conditions. In this study, a new analytical model called Fuel Rod and Cladding Analysis Subcode-Coated Cladding (FRACAS-CT), used to evaluate the mechanical behaviors of the ATF cladding, has been developed employing both thick-walled theory and some methodologies of the FRACAS. Based on the methodology of FRACAS, two different regimes of pellet-cladding mechanical interactions according to the gap status were considered as follows: one is an open-gap regime, and the other is a closed-gap regime. FRACAS-CT was developed within a limited scope of an elastic solution under the assumption of the prescribed creep, irradiation growth, and thermal deformation. To verify the FRACAS-CT model, an equivalent finite element (FE) model was proposed, and the mechanical behaviors of FRACAS-CT were compared with those of the equivalent FE model. As a result, it was determined that the stress results of FRACAS-CT are highly consistent with those of the equivalent FE model. Furthermore, FRACAS-CT was implemented into the FRAPCON4.0P1 code system to evaluate the mechanical behaviors of the ATF cladding using in-reactor conditions.