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.     

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.     

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。     

Cloud based cyber-physical systems: Network evaluation study

In recent years, the Industry 4.0 concept brings new demands and trends in different areas; one of them is distributing computational power to the cloud. This concept also introduced the Reference Architectural Model for Industry 4.0 (RAMI 4.0). The efficiency of data communications within the RAMI 4.0 model is a critical issue. Aiming to evaluate the efficiency of data communication in the Cloud Based Cyber-Physical Systems (CB-CPS), this study analyzes the periods and data amount required to communicate with individual hierarchy levels of the RAMI 4.0 model. The evaluation of the network properties of the communication protocols eligible for CB-CPS is presented. The network properties to different cloud providers and data centers’ locations have been measured and interpreted. To test the findings, an architecture for cloud control of laboratory model was proposed. It was found that the time of the day; the day of the week; and data center utilization have a negligible impact on latency. The most significant impact lies in the data center distance and the speed of the communication channel. Moreover, the communication protocol also has impact on the latency. The feasibility of controlling each level of RAMI 4.0 through cloud services was investigated. Experimental results showed that control is possible in many solutions, but these solutions mostly cannot depend just on cloud services. The intelligence on the edge of the network will play a significant role. The main contribution is a thorough evaluation of different cloud providers, locations, and communication protocols to provide recommendations sufficient for different levels of the RAMI 4.0 architecture.     

Al含量对Fe-xAl-4.0C-3.5Si合金组织及性能的影响

采用熔炼法制备了Fe-xAl-4.0C-3.5Si(x=6.3,7.4,8.9,11.1)系高温固体自润滑材料,研究了Al加入量对合金组织、力学性能及摩擦磨损性能的影响.结果表明:随着Al加入量的降低,合金中α-Fe(Al)固溶体的量增多,Fe3AlC0.5化合物的量减少,有利于改善材料的冲击韧度并提高合金的三点弯曲强度.Al加入量越少,凝固组织中石墨的面密度越大,可以减弱Al对液态合金中碳原子的排斥作用,有利于减小摩擦因素.其中Fe-7.4Al-4.0C-3.5Si合金因具有高的石墨面密度和Fe3AlC0.5硬质相均匀分布的组织特点,其耐磨与减摩性能最好,经900 ℃、15 h退火处理后,摩擦因数保持在0.32的较低水平,磨损率是QT400-18L球墨铸铁的1/5.     

基于WebGIS的邯郸水文信息管理系统

采用现代信息技术使公众及时准确地了解水文信息,成为水文管理发展的新趋势.运用WebGIS技术,实现水文信息的直观化、图像化发布,正是适应了这样的发展趋势.邯郸水文局根据自身特点,建立了邯郸水文信息管理系统,更好地服务于水利事业,是实现邯郸市水利信息化和现代化的一项重大举措.该文详细介绍了基于WebGIS的邯郸水文信息管理系统的设计及实现方法.     

用EVC4.0开发高效的Windows CE应用程序

本文介绍怎样开发高效的Windows CE应用程序.概述了消息的接收和分发机制,讨论了一些引起Windows CE程序开发不同于其他Windows 平台的独特特性.     

Optimal Deep Learning Model Enabled Secure UAV Classification forIndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.     

Visual Analytics for Exploring Local Impact of Air Traffic

The environmental and noise impact of airports often causes extensive political discussion which in some cases even lead to transnational tensions. Analyzing local approach and departure patterns around an airport is difficult since it depends on a variety of complex variables like weather, local and general regulations and many more. Yet, understanding these movements and the expected amount of flights during arrival and departure is of great interest to both casual and expert users, as planes have a higher impact on the areas beneath during these phases. We present a Visual Analytics framework that enables users to develop an understanding of local flight behavior through visual exploration of historical data and interactive manipulation of prediction models with direct feedback, as well as a classification quality visualization using a random noise metaphor. We showcase our approach using real world data from the Zurich International Airport region, where aircraft noise has led to an ongoing conflict between Germany and Switzerland. The use cases, findings and expert feedback demonstrate how our approach helps in understanding the situation and to substantiate the otherwise often subjective discourse on the topic.     

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with optimal wavelet kernel extreme learning machine (OWKELM) named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform. The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complexity and maximum detection accuracy. The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique, which incorporates the concepts of chaotic maps with CSOA. Besides, the OWKELM technique is applied for the intrusion detection and classification process. In addition, the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization (SFO) algorithm. The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance. In order to guarantee the supreme performance of the CCSOA-OWKELM technique, a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promising performance of the CCSOA-OWKELM technique over the recent state of art techniques.