面向产品型中小企业的CIMS体系结构与实施

分析了传统产品型中小企业中存在的瓶颈问题 ,提出一种面向产品型中小企业的CIMS体系结构 ,研究了其中管理信息系统的集成框架与实施方法、CAD CAPP CAM CAE系统的集成框架 ,以及该框架与管理信息系统的集成接口方案、制造自动化系统的实施方法等关键技术。作为应用示范 ,在较低投入的前提下 ,在较短时间内 ,完成了一家中小型纺机企业CIMS的实施 ,并取得了显著效果。实践证明 ,该CIMS体系结构具有简单、实用、成本低、实施周期短、见效快和易推广等特点。

CIMS网络需求分析辅助工具的研究与设计

着重阐述ＣＩＭＳ网络需求分析方法和辅助工具原型系统的实现方案，介绍了ＣＩＭＳ网络需求分析模板，并简要描述了相关的结构模型和信息分布算法。

CIMS网络子系统的规划设计

以一项ＣＩＭＳ应用工程为实例，讨论了ＣＩＭＳ关键技术之一——计算机网络子系统的规划设计方法。提出并分析了ＣＩＭＳ网络子系统规划设计中的共同技术原则，最后结合实例给出了一种层次化的规划设计方法和步骤。

CIMS网络辅助设计工具的实现模型

主要论述了CIMS网络辅助设计工具的实现模型。CIMS网络辅助设计工具由三部分组成 :①需求分析模块 ,它主要向用户提供标准的需求分析模板并对信息流量进行量化处理 ;②网络设计模块 ,它从需求分析模块获取相应的信息 ,自动生成初步的网络方案 ,使用者可以根据某些特殊的要求和自己的设计经验对该方案进行必要的调整 ;③性能评价模块 ,它对前面生成的方案进行性能分析、代价估算 ,并把结果反馈给使用者 ,以便再对方案进行调整。

CIMS环境中局域网网络安全子层设计

本文根据ＭＡＰ协议中网络层的无连接传输方式服务协议和开放系统工连参考模型的安全体系结构，在网络层用一个安全子层来实现数据的安全传输。并给出了该安全子层的协议处理功能和协议数据单元的格式，介绍了该子层传输和接收的过程。

基于域的CIMS网络设计方法的研究

今天企业都把网络系统作为信息集成技术的基础，面对网络系统的质量和数量日益增加的局面，由于网络设计和管理方面的复杂性、异构性和分布处理等一系列问题，迫切需要一种规范的设计方法和辅助工具。本文在研究ＣＩＭＳ网络设计方法与性能评价工具的基础上，从网络管理体系结构的角度出发，介绍了以域为特征的网络管理体系结构，探讨了以域为单位，按照目标－策略－计划－实施四个阶段，分层设计ＣＩＭＳ网络的方法。提出了用自顶向下和自底向上相结合的研究方法逐步明确设计思路。

一种面向CIMS的网络设计方法

本文首先介绍了CIMS中网络子系统所具有的特点,然后提出了一个包括流量分析调查方法、拓扑设计方法和平均延迟分析方法在内的较为实用的CIMS网络设计方法。

Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

This research work investigated comparative studies of expert system design and control of crude oil distillation column (CODC) using artificial neural networks based Monte Carlo (ANNBMC) simulation of random processes and artificial neural networks (ANN) model which were validated using experimental data obtained from functioning crude oil distillation column of Port-Harcourt Refinery, Nigeria by MATLAB computer program. Ninety percent (90%) of the experimental data sets were used for training while ten percent (10%) were used for testing the networks. The maximum relative errors between the experimental and calculated data obtained from the output variables of the neural network for CODC design were 1.98 error % and 0.57 error % when ANN only and ANNBMC were used respectively while their respective values for the maximum relative error were 0.346 error % and 0.124 error % when they were used for the controller prediction. Larger number of iteration steps of below 2500 and 5000 were required to achieve convergence of less than 10-7?for the training error using ANNBMC for both the design of the CODC and controller respectively while less than 400 and 700 iteration steps were needed to achieve convergence of 10-4?using ANN only. The linear regression analysis performed revealed the minimum and maximum prediction accuracies to be 80.65% and 98.79%;and 98.38% and 99.98% when ANN and ANNBMC were used for the CODC design respectively. Also, the minimum and maximum prediction accuracies were 92.83% and 99.34%;and 98.89% and 99.71% when ANN and ANNBMC were used for the CODC controller respectively as both methodologies have excellent predictions. Hence, artificial neural networks based Monte Carlo simulation is an effective and better tool for the design and control of crude oil distillation column.

Integrated Open Source Design for Architecture in High-Density Housing Practice

High-density housing can be interpreted as collections of individual units, which inevitably results in the dilemma between the global standardization designed by architects and local customization implemented by users. However, it is impossible to reflect the users＇ various needs in the conceptual design stage for high-density housing because of the economic, industrial and time constrains. In response to this challenge, this research paper outlines a different high-density housing design approach that can adopt users＇ individual customization in the conceptual design stage during the housing design practice. Hence, the design process would be an open-ended evolutionary and transparent process rather than deterministic executions as we have now in most high-density cities, such as Hong Kong. In order to overcome the deficiency in addressing future uncertainties of different users and address the issues of one-off developments without iterating users＇ feedback in the housing practice, this essay proposes IOSDA （integrated open source design for architecture） for the design practice of high-density housing, through collective data and parametric connectivity between users and architects. IOSDA reflects a different design attitude towards the future, i.e., to shift from architects＇ heroic prediction of the future to collective engagement of the present with more robust capacities for new possibilities.

An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network

Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in that process.One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,online.This paper presents a novel chronic disease prediction system based on an incremental deep neural network.The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner.With time,the system can predict diabetes more and more accurately by processing the feedback information.Many diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input attributes.In order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was created.Users’data collected by different sensors were used to train the network model.We evaluated our system using a real-world diabetes dataset to confirm its effectiveness.The experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.

一种农业自动化灌溉系统的高效能设计研究

以进一步提升农业自动化灌溉系统的作业效率为研究目标,选取其核心结构组件布局作为研究对象,基于通信控制优化理念展开高效能设计研究。结合自动化灌溉系统的运行机理,从通信网络架构分层角度入手,建立灌溉系统参数计算处理模型。针对系统软件控制和硬件配置完成优化,选定灌溉作物进行灌溉试验,结果表明:此通信网络架构理念下的控制优化布局,可实现灌溉系统的响应准确率与决策准确率同步提升,系统运行稳定有效;整体系统的灌溉效率可达94.55%,满足智能化灌溉控制设计要求,具有很好的应用推广价值。研究结果对于灌溉系统向智能化精准化提升有较好的启示意义。

UltraStar:A Lightweight Simulator of Ultra-Dense LEO Satellite Constellation Networking for 6G

The mega-constellation network has gained significant attention recently due to its great potential in providing ubiquitous and high-capacity connectivity in sixth-generation(6G)wireless communication systems.However,the high dynamics of network topology and large scale of mega-constellation pose new challenges to the constellation simulation and performance evaluation.In this paper,we introduce UltraStar,a lightweight network simulator,which aims to facilitate the complicated simulation for the emerging mega-constellation of unprecedented scale.Particularly,a systematic and extensible architecture is proposed,where the joint requirement for network simulation,quantitative evaluation,data statistics and visualization is fully considered.For characterizing the network,we make lightweight abstractions of physical entities and models,which contain basic representatives of networking nodes,structures and protocol stacks.Then,to consider the high dynamics of Walker constellations,we give a two-stage topology maintenance method for constellation initialization and orbit prediction.Further,based on the discrete event simulation(DES)theory,a new set of discrete events is specifically designed for basic network processes,so as to maintain network state changes over time.Finally,taking the first-generation Starlink of 11927 low earth orbit(LEO)satellites as an example,we use UltraStar to fully evaluate its network performance for different deployment stages,such as characteristics of constellation topology,performance of end-to-end service and effects of network-wide traffic interaction.The simulation results not only demonstrate its superior performance,but also verify the effectiveness of UltraStar.

DEVELOPMENT AND PROSPECT OF GENERALHOPSITAL ARCHITECTURE IN CHINA

This paper presents the development and prospects of hospital architec-ture in China.Some typical hospitals in different periods are introduced, and the fu-ture development of huspital construction is discussed.

A Novel Design Method for Information Services

IT （information technology） services are diverse and complex. Numerous service design methods have been developed for designing and developing products. However, owing to the limited availability of useful tools in IT service design, these methods are ineffective. This study proposes an innovative three-dimensional method for designing IT services. The proposed method considers user requirements, an organization＇s business requirements, service providers＇ management requirements as well as the decision-making criteria of management representatives to ensure a smooth implementation of a designed IT service. Using this method, a prototype system to improve campus wireless local area network services was developed and tested at a university in Taiwan. The prototype system reduced the need for repetitive authentication and the time required to solve service problems and address user complaints. In addition, the service design team observed an approximately 70% reduction in project cycle time. The proposed method provided a systematic means to organize the design and implementation of IT services.

基于生成对抗网络的植物景观生成设计——以花境平面图生成为例

【目的】植物景观设计需要科学性和技术性兼备。探索人工智能,特别是生成对抗网络(generative adversarial network,GAN)在植物景观设计中的应用,能够帮助设计师提高设计过程的效率。【方法】以花境平面图生成设计为例,建立了基于细致筛选优化的植物平面数据集。数据集标注基于植物分类,考虑了植物的种类、搭配原则及空间布局规律。引入循环生成对抗网络(cycle generative adversarial network,CycleGAN)模型对数据集进行学习,实现花境平面设计的自动生成。【结果】CycleGAN模型在以花境为代表的植物景观设计中具有独特的优势,花境平面图生成模型能够准确识别条形场地边界,并在色彩再现方面表现出较高的精度和可识别性。生成平面图的空间布局中,色块大小、平面布局形态和位置展示了各种植物的空间分布特点,并能够复现部分潜在搭配组合,生成了符合美学和生态原则的设计方案。然而,模型在部分场地边框的准确识别和设计结果的多样性方面仍存在局限。【结论】证明了CycleGAN在植物景观设计领域的应用潜力,并为实践中的植物景观设计提供了创新和有效的解决方案。

航天网络信息体系云原生架构设计

云原生技术赋能航天网络信息体系基础设施建设,可提升航天信息系统的灵活适应性。以分布式云原生网络信息体系敏捷开发机制为主线,设计了航天网络信息体系云原生架构。首先,分析了云原生基础设施技术体系;然后,提出了由云边协同底座、云原生基础设施平台和云原生应用构成的航天网络信息体系总体架构方案;最后,对云边协同底座与云原生基础设施平台进行了设计,从而为我国航天网络信息体系架构设计及应用提供参考。

生成式算法在风景园林生成设计中的适应性研究

【目的】测试目前常见的生成式算法在风景园林生成设计中的表现,尝试探讨不同的生成式算法在各个设计环节应用的优劣势。【方法】构建“方案文本生成—场地布局生成—平面图渲染—效果图生成”的风景园林方案生成设计理论框架,在同一个输入条件下测试不同算法在各个设计环节的表现。【结果】方案文本生成算法方面,测试了大语言模型及其在下游应用中的适应性调整方法,包括二次预训练及微调、检索增强生成、多代理系统的方法;图像生成算法方面,测试了大型文生图预训练模型及微调方法、图像结构控制方法;另外,将文生图模型与基于生成对抗网络的布局生成算法、参数化三维模型生成系统结合,并测试其在各个阶段的表现。【结论】目前大型预训练模型以其在通用领域的生成能力为生成设计领域提供强有力的支撑,但预训练模型提供的只是基础的通用能力,从通用领域到风景园林领域的适应性研究还需要检索、控制、微调等多种技术手段协作以开发全面的技术体系。

轻量级深度神经网络模型适配边缘智能研究综述

随着物联网和人工智能的迅猛发展,边缘计算和人工智能的结合催生了边缘智能这一新的研究领域。边缘智能具备一定的计算能力,能够提供实时、高效和智能的响应。它在智能城市、工业物联网、智能医疗、自动驾驶以及智能家居等领域都具有重要的应用。为了提升模型的准确度,深度神经网络往往采用更深、更大的架构,导致了模型参数的显著增加、存储需求的上升和计算量的增大。受限于物联网边缘设备在计算能力、存储空间和能源资源方面的局限,深度神经网络难以被直接部署到这些设备上。因此,低内存、低计算资源、高准确度且能实时推理的轻量级深度神经网络成为了研究热点。文中首先回顾边缘智能的发展历程,并分析轻量级深度神经网络适应边缘智能的现实需求,提出了两种构建轻量级深度神经网络模型的方法:深度模型压缩技术和轻量化架构设计。接着详细讨论了参数剪枝、参数量化、低秩分解、知识蒸馏以及混合压缩5种主要的深度模型压缩技术,归纳它们各自的性能优势与局限,并评估它们在常用数据集上的压缩效果。之后深入分析轻量化架构设计中的调整卷积核大小、降低输入通道数、分解卷积操作和调整卷积宽度的策略,并比较了几种常用的轻量化网络模型。最后,展望轻量级深度神经网络在边缘智能领域的未来研究方向。

生成对抗网络在建筑设计领域的应用研究综述

随着计算机科学的发展,人工智能应用渗透各个领域。为探究生成对抗网络在建筑设计领域的应用,本文将现有的国内外相关研究成果按照不同主题,分为建筑设计与生成、设计优化与预测、建筑风格转换与设计灵感、数据增强与评估四大类,并在此基础上,分析生成对抗网络在建筑设计领域的发展现状与未来的研究趋势,提出其未来可能的应用方向与潜在挑战,为研究者提供新的思路和灵感,从而推动建筑设计领域的创新和发展。

轻量化深度卷积神经网络设计研究进展

轻量化设计是解决深度卷积神经网络(deep convolutional neural network,DCNN)对设备性能和硬件资源依赖性的流行范式,轻量化的目的是在不牺牲网络性能的前提下,提高计算速度和减少内存占用。综述了DCNN的轻量化设计方法,着重回顾了近年来DCNN的研究进展,包括体系设计和模型压缩两大轻量化策略,深入比较了这两类方法的创新性、优势与局限性,并探讨了支撑轻量化模型的底层框架。此外,对轻量化网络已经成功应用的场景进行了描述,并对DCNN轻量化的未来发展趋势进行了预测,旨在为深度卷积神经网络的轻量化研究提供有益的见解和参考。