基于Device Net总线的造纸机传动系统设计

把现场总线Device Net应用到纸机控制系统中,通过硬件设计实现整个控制系统的PLC配线和与变频器的硬件连接,根据功能连接和总线通信的需要对变频器进行相关参数设置,并在此基础上对纸机控制系统的软件进行设计,实现纸机传动中加、减速等基本操作控制、速度链控制及负荷分配控制等功能。

基于Device Net的现场总线设备及其开发

Device Net是一种基于CAN总线技术的符合全球工业标准的开放型通信网络。可以连接底层低端工业设备,又可以连接像变频器、操作员终端等复杂设备。介绍了Device Net网络模型、技术特点、基于Device Net的总线设备对象模型以及节点开发的途径及一般步骤,建立了节点开发实例并介绍了其软、硬件设计流程。

Danfoss变频器在Device Net总线控制中的应用

介绍了通过Device Net总线通信方式控制变频器的优越性,并详细分析了Darffoss变频器连接Device Net网络的具体参数设置及数据交换方式。

Device Net在低压配电控制系统中的应用

本文回顾了 Device Net现场总线的发展概况 ,分析了该总线协议的主要内容。根据我国低压配电控制过程的特点 ,针对现有的具有 Device Net通信接口的低压电器产品 ,提出了实现基于 Device

无线Device Net控制系统在移载车中的应用

根据以往运输线设计理念和使用效果,设计人员综合客户和创新设计要求,将铅阳极板运输线的运输小车采用无线Device Net网络与PLC之间的通讯方式,一改传统的有线网络模式,避免了有线网络的诸多弊端,此方案设计新颖,通讯快速灵敏,维护简洁方便。

Danfoss FC200变频器与AB PLC的Device Net通讯的实现

本文介绍了曹妃甸海水淡化项目中Danfoss FC200变频器与AB PLC的Device Net现场总线通讯的参数设置和软件编程方法,Device Net网络是工业控制的底层网络,采用了数据网络通信的新技术,具有低成本,高效率和可靠性高的特点。

An Environment‑Tolerant Ion‑Conducting Double‑Network Composite Hydrogel for High‑Performance Flexible Electronic Devices

High-performance ion-conducting hydrogels(ICHs)are vital for developing flexible electronic devices.However,the robustness and ion-conducting behavior of ICHs deteriorate at extreme tempera-tures,hampering their use in soft electronics.To resolve these issues,a method involving freeze–thawing and ionizing radiation technology is reported herein for synthesizing a novel double-network(DN)ICH based on a poly(ionic liquid)/MXene/poly(vinyl alcohol)(PMP DN ICH)system.The well-designed ICH exhibits outstanding ionic conductivity(63.89 mS cm^(-1) at 25℃),excellent temperature resistance(-60–80℃),prolonged stability(30 d at ambient temperature),high oxidation resist-ance,remarkable antibacterial activity,decent mechanical performance,and adhesion.Additionally,the ICH performs effectively in a flexible wireless strain sensor,thermal sensor,all-solid-state supercapacitor,and single-electrode triboelectric nanogenerator,thereby highlighting its viability in constructing soft electronic devices.The highly integrated gel structure endows these flexible electronic devices with stable,reliable signal output performance.In particular,the all-solid-state supercapacitor containing the PMP DN ICH electrolyte exhibits a high areal specific capacitance of 253.38 mF cm^(-2)(current density,1 mA cm^(-2))and excellent environmental adaptability.This study paves the way for the design and fabrication of high-performance mul-tifunctional/flexible ICHs for wearable sensing,energy-storage,and energy-harvesting applications.

In Situ Atomic Reconstruction Engineering Modulating Graphene-Like MXene-Based Multifunctional Electromagnetic Devices Covering Multi-Spectrum

With the diversified development of big data,detection and precision guidance technologies,electromagnetic(EM)functional materials and devices serving multiple spectrums have become a hot topic.Exploring the multispectral response of materials is a challenging and meaningful scientific question.In this study,MXene/TiO_(2)hybrids with tunable conduction loss and polarization relaxation are fabricated by in situ atomic reconstruction engineering.More importantly,MXene/TiO_(2)hybrids exhibit adjustable spectral responses in the GHz,infrared and visible spectrums,and several EM devices are constructed based on this.An antenna array provides excellent EM energy harvesting in multiple microwave bands,with|S11|up to−63.2 dB,and can be tuned by the degree of bending.An ultra-wideband bandpass filter realizes a passband of about 5.4 GHz and effectively suppresses the transmission of EM signals in the stopband.An infrared stealth device has an emissivity of less than 0.2 in the infrared spectrum at wavelengths of 6-14μm.This work can provide new inspiration for the design and development of multifunctional,multi-spectrum EM devices.

BLS-identification:A device fingerprint classification mechanism based on broad learning for Internet of Things

The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.

Unconventional room-temperature negative magnetoresistance effect in Au/n-Ge:Sb/Au devices

Non-magnetic semiconductor materials and their devices have attracted wide attention since they are usually prone to exhibit large positive magnetoresistance(MR)effect in a low static magnetic field environment at room temperature.However,how to obtain a large room-temperature negative MR effect in them remains to be studied.In this paper,by designing an Au/n-Ge:Sb/Au device with metal electrodes located on identical side,we observe an obvious room-temperature negative MR effect in a specific 50 T pulsed high magnetic field direction environment,but not in a static low magnetic field environment.Through the analysis of the experimental measurement of the Hall effect results and bipolar transport theory,we propose that this unconventional negative MR effect is mainly related to the charge accumulation on the surface of the device under the modulation of the stronger Lorentz force provided by the pulsed high magnetic field.This theoretical analytical model is further confirmed by regulating the geometry size of the device.Our work sheds light on the development of novel magnetic sensing,magnetic logic and other devices based on non-magnetic semiconductors operating in pulsed high magnetic field environment.

Privacy Preservation in IoT Devices by Detecting Obfuscated Malware Using Wide Residual Network

The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.

Liquid Metal Grid Patterned Thin Film Devices Toward Absorption‑Dominant and Strain‑Tunable Electromagnetic Interference Shielding

The demand of high-performance thin-film-shaped deformable electromagnetic interference(EMI)shielding devices is increasing for the next generation of wearable and miniaturized soft electronics.Although highly reflective conductive materials can effectively shield EMI,they prevent deformation of the devices owing to rigidity and generate secondary electromagnetic pollution simultaneously.Herein,soft and stretchable EMI shielding thin film devices with absorption-dominant EMI shielding behavior is presented.The devices consist of liquid metal(LM)layer and LM grid-patterned layer separated by a thin elastomeric film,fabricated by leveraging superior adhesion of aerosol-deposited LM on elastomer.The devices demonstrate high electromagnetic shielding effectiveness(SE)(SE_(T) of up to 75 dB)with low reflectance(SER of 1.5 dB at the resonant frequency)owing to EMI absorption induced by multiple internal reflection generated in the LM grid architectures.Remarkably,the excellent stretchability of the LM-based devices facilitates tunable EMI shielding abilities through grid space adjustment upon strain(resonant frequency shift from 81.3 to 71.3 GHz@33%strain)and is also capable of retaining shielding effectiveness even after multiple strain cycles.This newly explored device presents an advanced paradigm for powerful EMI shielding performance for next-generation smart electronics.

Quality of Service and Security on Cisco Network Devices, Coupled with the Development of a Mobile Application Prototype Software for Server Room Temperature Monitoring

In an era where digital technology is paramount, higher education institutions like the University of Zambia (UNZA) are employing advanced computer networks to enhance their operational capacity and offer cutting-edge services to their academic fraternity. Spanning across the Great East Road campus, UNZA has established one of the most extensive computer networks in Zambia, serving a burgeoning community of over 20,000 active users through a Metropolitan Area Network (MAN). However, as the digital landscape continues to evolve, it is besieged with burgeoning challenges that threaten the very fabric of network integrity—cyber security threats and the imperatives of maintaining high Quality of Service (QoS). In an effort to mitigate these threats and ensure network efficiency, the development of a mobile application to monitor temperatures in the server room was imperative. According to L. Wei, X. Zeng, and T. Shen, the use of wireless sensory networks to monitor the temperature of train switchgear contact points represents a cost-effective solution. The system is based on wireless communication technology and is detailed in their paper, “A wireless solution for train switchgear contact temperature monitoring and alarming system based on wireless communication technology”, published in the International Journal of Communications, Network and System Sciences, vol. 8, no. 4, pp. 79-87, 2015 [1]. Therefore, in this study, a mobile application technology was explored for monitoring of temperatures in the server room in order to aid Cisco device performance. Additionally, this paper also explores the hardening of Cisco device security and QoS which are the cornerstones of this study.

基于DeviceNet总线的数字转换卡研究与实现

介绍了 DeviceNet 的通信协议和对象建模方法,给出了应用层软件设计步骤,设计出了 DeviceNet 通信接口的硬件电路,并将其应用在传统仪表中,使模拟信号可靠地转换成符合 DeviceNet 总线协议的数字信号,这种仪表能和带有 DeviceNet 网卡的计算机组成主从连接,进行网络通信,实现仪表的智能化,数字化,网络化。

基于单片机的DeviceNet现场总线从站通信接口的开发

DeviceNet 是一种工业现场底层设备互联技术,本文详细介绍了以单片机为平台的 DeviceNet 现场总线从站接口的开发,并给出了详细的硬件电路和软件设计流程。

基于Device Net总线系统的底漆烘干输送自动线控制设计

基于Device Net总线系统的底漆烘干输送自动线控制系统设计项目,是来源于风神襄樊汽车有限公司实际生产需求的发展计划。本论文阐述了此项目设计研制开发全部内容:该项目系统硬件是以OMRON PLC作为主站,用Device Net现场总线将几种类型多个远程总线模块与主站连接,实现主站PLC对自动线中的多设备的远程实时控制;完成了与底漆悬链上料联锁控制、与底漆烘干炉入口两台读卡机联锁控制;完成了两条底漆烘干炉分合流控制以及底漆烘干炉各种设备有效控制。实际运行情况表明,该系统大大提高了生产效率,有很高的性价比。

Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

Fedora-type magnetic compression anastomosis device for intestinal anastomosis

BACKGROUND Although previous studies have confirmed the feasibility of magnetic compression anastomosis(MCA),there is still a risk of long-term anastomotic stenosis.For traditional MCA devices,a large device is associated with great pressure,and eventually increased leakage.AIM To develop a novel MCA device to simultaneously meet the requirements of pressure and size.METHODS Traditional nummular MCA devices of all possible sizes were used to conduct ileac anastomosis in rats.The mean(±SD)circumference of the ileum was 13.34±0.12 mm.Based on short-and long-term follow-up results,we determined the appropriate pressure range and minimum size.Thereafter,we introduced a novel“fedora-type”MCA device,which entailed the use of a nummular magnet with a larger sheet metal.RESULTS With traditional MCA devices,the anastomoses experienced stenosis and even closure during the long-term follow-up when the anastomat was smaller thanΦ5 mm.However,the risk of leakage increased when it was larger thanΦ4 mm.On comparison of the different designs,it was found that the“fedora-type”MCA device should be composed of aΦ4-mm nummular magnet with aΦ6-mm sheet metal.CONCLUSION The diameter of the MCA device should be greater than 120%of the enteric diameter.The novel“fedora-type”MCA device controls the pressure and optimizes the size.

DeviceNet现场总线在粘胶短纤维纺丝工段的应用

主要论述了利用Device NET现场总线在粘胶短纤维纺丝工段控制系统中的应用,以及PLC、变频器、触摸屏和工控机的组态。

A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P protocols.Using P2P networks ensures scalability and removes the need for centralized management.However,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s ratings.This compromises users’safety and makes them lose their confidence about the files or services they are receiving.To address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious.In this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in classification.The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.