基于OPC UA的虚实结合实验设计与实践

OPCUA是一种面向未来的工业通信技术,是实现智能制造的关键技术之一,在工业网络系统中得到越来越广泛的应用。为促进教学资源同步技术发展,以OPCUA技术为出发点,设计了基于OPCUA通信的客户端仿真、系统虚拟仿真、实践操作三个进阶实验项目。项目开发充分利用行业已有客户端测试软件和虚拟仿真软件资源,兼顾线上、线下学习,内容由基础到综合、虚实结合,旨在提升学生新技术应用能力和自主创新能力,更好地服务于智能制造转型升级和发展,为相关专业的实践教学及资源开发提供参考。

基于OPC UA的纤维缠绕机信息模型开发和应用

为实现对复合材料纤维缠绕机生产过程的监测,以缠绕机及其辅助设备为研究对象,针对实际加工设备的多样性造成的加工数据采集的异构性,基于OPC UA建立缠绕机的信息模型架构。根据OPC基金会发布的OPC UA规范和相关的行业标准,建立纤维缠绕机系统的信息模型和实例化。基于开源项目open62541,导入编译后的缠绕机的信息模型代码,自定义开发出符合使用规范的OPC UA服务器,然后利用UA客户端实现对服务器的连接,客户端能够通过服务器查询系统的加工数据,解决加工数据的异构性问题以达到监测缠绕机生产加工过程的目的;最后验证了建立的纤维缠绕机信息模型能够正确导入到开源项目open62541中,实现了加工数据监测。

蒲金口服液对宫内感染/炎症致早产脑损伤大鼠脑组织OL、OPC细胞凋亡和Nogo-A、OMgp表达的影响

目的探讨蒲金口服液对宫内感染/炎症致早产脑损伤大鼠神经保护作用的机制。方法将80只胎龄<22 d的早产仔鼠随机分为8组(每组10只):早期药物干预组(蒲金口服液高剂量组、蒲金口服液低剂量组、银杏叶提取物组、模型组)和晚期药物干预组。生后21 d龄进行神经行为学检测,并采用qRT-PCR和Western blot方法检测勿动蛋白-A(Nogo-A)和少突胶质细胞髓磷脂糖蛋白(OMgp)的表达,TUNEL法和A2B5、CNPase双标免疫荧光染色法检测少突胶质细胞(OL)、前体细胞(OPC)凋亡情况。结果LPS组早产仔鼠脑组织Nogo-A和OMgpmRNA和蛋白的表达均明显高于正常对照组(P<0.05),蒲金口服液高剂量、低剂量和银杏叶提取物干预组大鼠的神经行为学评分均优于模型组(P<0.05),且早期干预组高于晚期干预组(P<0.05)。蒲金口服液高剂量、低剂量和银杏叶提取物均能降低早产脑损伤大鼠脑组织Nogo-A、OMgp mRNA和蛋白的表达,减少OL和OPC的凋亡(P<0.05),其中蒲金口服液高剂量早期干预效果更显著(P<0.05)。结论蒲金口服液可以改善早产脑损伤大鼠的神经行为学表现,减少脑组织Nogo-A、OMgp的表达和OL、OPC的凋亡,具有神经保护的作用。

基于OPCE理念的无机化学专创融合教学模式研究

为培养适应社会发展要求的高素质应用型人才,许昌学院化工与材料学院秉承“开放·实践·创新教育”(OPCE,Open,Practical and Creative Education)理念,将无机化学课程知识教育和创新创业教育深度融合,构建了无机化学专创融合教学模式。该课程教学模式的实施培养了学生的创新精神、创业意识和创新创业能力,进而提高了我院应用型人才的培养质量。本文总结了无机化学专创融合教学模式建设的经验,以期为地方高校专创融合课程建设和应用型人才培养提供一定启示。

基于OPC通信协议的采煤机远程监控系统研究

为了解决井下综采工作面自动化设备种类繁多、不同硬件之间通信协议不统一的问题,从软件的角度出发,开发了一种基于OPC通信协议的服务器。该服务器能够集成多种通信协议,如Modbus、Fins等,以实现井下不同设备之间的数据信息交换和协调。通过集成Modbus/TCP通信协议开发了采煤机远程监控系统,并进行了一系列相关测试。实验结果表明,开发的OPC服务器及采煤机远程监控系统可以实现采煤机的实时监控。

OPC技术在RobotStudio与PLC通讯上的应用

针对ABB机器人离线编程调试软件RobotStudio无法与PLC直接通讯的问题,采用RobotStudio与PLC基于OPC技术通讯的方法,即添加OPC客户端功能,将RobotStudio中的焊接门架位置参数与机器人姿态参数通过OPC服务器软件KEPServerEX经以太网传输给PLC,并通过PLC进行焊接门架的参数化控制。经仿真测试,该方案成功解决了RobotStudio与PLC的通讯问题。

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

Timing effect of high temperature exposure on the plasticity of internode and plant architecture in maize

The occurrence of high temperature(HT)in crop production is becoming more frequent and unpredictable with global warming,severely threatening food security.The state of an organ’s growth and development is largely determined by the temperature conditions it is exposed to over time.Maize is the main cereal crop,and its stem growth and plant architecture are closely related to lodging resistance,and especially sensitive to temperature.However,systematic research on the timing effect of HT on the sequentially developing internode and stem is currently lacking.To identify the timing effect of HT on the morphology and plasticity of the stem in maize,two hybrids(Zhengdan 958(ZD958),Xianyu 335(XY335))characterized by distinct morphological traits in the stem were exposed to a 7-day HT treatment from the V6 to V17 stages(Vn presents the vegetative stage with n leaves fully expanded)in 2019-2020.The results demonstrated that exposure to HT during V6-V12 accelerated the rapid elongation of stems.For instance,HT occurring at V7 and V12 specifically promoted the lengths and weights of the 3rd-5th and 9th-11th internodes,respectively.Meanwhile,HT slowed the growth of internodes adjacent to the promoted internodes.Interestingly,compared with control,the plant height was significantly increased soon after HT treatment,but the promotion effect became narrower at the subsequent flowering stage,demonstrating a self-adjusting mechanism in the maize plant in response to HT.Importantly,HT altered the plant architectures,including a rising of the ear position and increase in the ear position coefficient.XY335 exhibited greater sensitivity in stem development than ZD958 under HT treatment.These findings improve our systematic understanding of the plasticity of internode and plant architecture in response to the timing of HT exposure.

A 48-bp deletion upstream of LIGULELESS 1 alters rice panicle architecture

Panicle architecture is an agronomic determinant of crop yield and a target for cereal crop improvement.To investigate its molecular mechanisms in rice,we performed map-based cloning and characterization of OPEN PANICLE 1(OP1),a gain-of-function allele of LIGULELESS 1(LG1),controlling the spread-panicle phenotype.This allele results from a 48-bp deletion in the LG1 upstream region and promotes pulvinus development at the base of the primary branch.Increased OP1 expression and altered panicle phenotype in chimeric transgenic plants and upstream-region knockout mutants indicated that the deletion regulates spread-panicle architecture in the mutant spread panicle 1(sp1).Knocking out BRASSINOSTEROID UPREGULATED1(BU1)gene in the background of OP1 complementary plants resulted in compact panicles,suggesting OP1 may regulate inflorescence architecture via the brassinosteroid signaling pathway.We regard that manipulating the upstream regulatory region of OP1 or genes involved in BR signal pathway could be an efficient way to improve rice inflorescence architecture.

Enhancing MXene-based supercapacitors:Role of synthesis and 3D architectures

MXene has been the limelight for studies on electrode active materials,aiming at developing supercapacitors with boosted energy density to meet the emerging influx of wearable and portable electronic devices.Despite its various desirable properties including intrinsic flexibility,high specific surface area,excellent metallic conductivity and unique abundance of surface functionalities,its full potential for electrochemical performance is hindered by the notorious restacking phenomenon of MXene nanosheets.Ascribed to its two-dimensional(2D)nature and surface functional groups,inevitable Van der Waals interactions drive the agglomeration of nanosheets,ultimately reducing the exposure of electrochemically active sites to the electrolyte,as well as severely lengthening electrolyte ion transport pathways.As a result,energy and power density deteriorate,limiting the application versatility of MXene-based supercapacitors.Constructing 3D architectures using 2D nanosheets presents as a straightforward yet ingenious approach to mitigate the fatal flaws of MXene.However,the sheer number of distinct methodologies reported,thus far,calls for a systematic review that unravels the rationale behind such 3D MXene structural designs.Herein,this review aims to serve this purpose while also scrutinizing the structure–property relationship to correlate such structural modifications to their ensuing electrochemical performance enhancements.Besides,the physicochemical properties of MXene play fundamental roles in determining the effective charge storage capabilities of 3D MXene-based electrodes.This largely depends on different MXene synthesis techniques and synthesis condition variations,hence,elucidated in this review as well.Lastly,the challenges and perspectives for achieving viable commercialization of MXene-based supercapacitor electrodes are highlighted.

Sedimentary architecture of submarine channel-lobe systems under different seafloor topography:Insights from the Rovuma Basin offshore East Africa

Seafloor topography plays an important role in the evolution of submarine lobes.However,it is still not so clear how the shape of slope affects the three-dimensional(3-D)architecture of submarine lobes.In this study,we analyze the effect of topography factors on different hierarchical lobe architectures that formed during Pliocene to Quaternary in the Rovuma Basin offshore East Africa.We characterize the shape,size and growth pattern of different hierarchical lobe architectures using 3-D seismic data.We find that the relief of the topographic slope determines the location of preferential deposition of lobe complexes and single lobes.When the topography is irregular and presents topographic lows,lobe complexes first infill these depressions.Single lobes are deposited preferentially at positions with higher longitudinal(i.e.across-slope)slope gradients.As the longitudinal slope becomes higher,the aspect ratio of the single lobes increases.Lateral(i.e.along-slope)topography does not seem to have a strong influence on the shape of single lobe,but it seems to affect the overlap of single lobes.When the lateral slope gradient is relatively high,the single lobes tend to have a larger overlap surface.Furthermore,as the average of lateral slope and longitudinal slope gets greater,the width/thickness ratio of the single lobe is smaller,i.e.sediments tend to accumulate vertically.The results demonstrate that the shape of slopes more comprehensively influences the 3-D architecture of lobes in natural deep-sea systems than previously other lobe deposits and analogue experiments,which helps us better understand the development and evolution of the distal parts of turbidite systems.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Enhanced Differentiable Architecture Search Based on Asymptotic Regularization

In differentiable search architecture search methods,a more efficient search space design can significantly improve the performance of the searched architecture,thus requiring people to carefully define the search space with different complexity according to various operations.Meanwhile rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture search.With this in mind,we propose a faster and more efficient differentiable architecture search method,AllegroNAS.Firstly,we introduce a more efficient search space enriched by the introduction of two redefined convolution modules.Secondly,we utilize a more efficient architectural parameter regularization method,mitigating the overfitting problem during the search process and reducing the error brought about by gradient approximation.Meanwhile,we introduce a natural exponential cosine annealing method to make the learning rate of the neural network training process more suitable for the search procedure.Moreover,group convolution and data augmentation are employed to reduce the computational cost.Finally,through extensive experiments on several public datasets,we demonstrate that our method can more swiftly search for better-performing neural network architectures in a more efficient search space,thus validating the effectiveness of our approach.

Application of Artificial Intelligence Technology in Landscape Architecture Industry

With wide application prospects in landscape industry,artificial intelligence technology plays an important role in improving work efficiency,optimizing design,strengthening construction management,and achieving intelligent maintenance.With the continuous development of technology,the application of artificial intelligence in landscape architecture industry will become more in-depth and extensive,which can provid powerful support for the innovation and development of the industry.It is hoped that the modernization process of the landscape industry can be promoted through the analysis on the application and difficulties of artificial intelligence technology in the landscape industry.

Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.

Multilevel carbon architecture of subnanoscopic silicon for fast‐charging high‐energy‐density lithium‐ion batteries

Silicon(Si)is widely used as a lithium‐ion‐battery anode owing to its high capacity and abundant crustal reserves.However,large volume change upon cycling and poor conductivity of Si cause rapid capacity decay and poor fast‐charging capability limiting its commercial applications.Here,we propose a multilevel carbon architecture with vertical graphene sheets(VGSs)grown on surfaces of subnanoscopically and homogeneously dispersed Si–C composite nanospheres,which are subsequently embedded into a carbon matrix(C/VGSs@Si–C).Subnanoscopic C in the Si–C nanospheres,VGSs,and carbon matrix form a three‐dimensional conductive and robust network,which significantly improves the conductivity and suppresses the volume expansion of Si,thereby boosting charge transport and improving electrode stability.The VGSs with vast exposed edges considerably increase the contact area with the carbon matrix and supply directional transport channels through the entire material,which boosts charge transport.The carbon matrix encapsulates VGSs@Si–C to decrease the specific surface area and increase tap density,thus yielding high first Coulombic efficiency and electrode compaction density.Consequently,C/VGSs@Si–C delivers excellent Li‐ion storage performances under industrial electrode conditions.In particular,the full cells show high energy densities of 603.5 Wh kg^(−1)and 1685.5 Wh L^(−1)at 0.1 C and maintain 80.7%of the energy density at 3 C.

基于OPC UA和RobotStudio的工业机器人工作站集成仿真

针对工业机器人工作站现场集成调试周期长、效率低的问题,基于OPC UA服务器将RobotStudio平台与西门子虚拟PLC控制器建立通信,搭建搬运码垛工作站集成仿真调试环境。在RobotStudio中设计动态工具的Smart组件,离线规划和编写机器人运动轨迹和程序;使用TIA软件编写工作站逻辑程序并下载至虚拟控制器;通过OPC UA建立连接,再利用RobotStudio完成工作站逻辑设计、调试和运动节拍优化。结果表明:该仿真能够有效验证工业机器人运动轨迹和PLC逻辑程序控制的合理性和准确性;调试机器人TCP与外围设备的速度能优化工作站的运动节拍,缩短了工作站现场集成调试周期。为降低工作站集成成本和提高生产效率提供理论依据。

One and Half Centuries of Church Architecture in Ibadan,Nigeria

By the end of the 19th century,the missionaries had successfully planted Christianity in Ibadan,Nigeria with its distinguishable architecture.The history of church architecture in Ibadan is synonymous with the beginning of Christianity in Yoruba Country.Based on historical data obtained from the archives and field investigations,the paper traces the development of the church buildings of the missionary churches in Ibadan from the late 19th century to early 21st century.With shifts in the thinking about ideological and liturgical issues in church architecture globally,three distinct phases of development of church buildings in Ibadan are suggested beginning with the mission stations and ending with the modernist church buildings.The paper concludes that attempts to introduce modern architectural expressions in church buildings in Ibadan were increasingly common and strong but traditionalist and historicist approaches to architectural planning of church buildings remained predominant into the 21st century.

Exploration of Graduate Student Cultivation Mode of Landscape Architecture under the Background of“Artificial Intelligence+X”

Under the background of“artificial intelligence+X”,the development of landscape architecture industry ushers in new opportunities,and professional talents need to be updated to meet the social demand.This paper analyzes the cultivation demand of landscape architecture graduate students in the context of the new era,and identifies the problems by comparing the original professional graduate training mode.The new cultivation mode of graduate students in landscape architecture is proposed,including updating the target orientation of the discipline,optimizing the teaching system,building a“dualteacher”tutor team,and improving the“industry-university-research-utilization”integrated cultivation,so as to cultivate high-quality compound talents with disciplinary characteristics.

OPC技术在工业LED显示屏中的应用

主要介绍如何利用OPC技术实现把现场信息数据实时显示在工业LED显示屏上,通过编写软件实现WinCC与LED显示屏的信息交换,具有很高的推广价值。