Robustness Design for CNN Templates with Performance of Extracting Closed Domain

The cellular neural/nonlinear network （CNN） is a powerful tool for image and video signal processing, robotic and biological visions. This paper introduces a kind of CNNs with performance of extracting closed domains in binary images, and gives a general method for designing templates of such a kind of CNNs. One theorem provides parameter inequalities for determining parameter intervals for implementing prescribed image processing functions, respectively. Examples for extracting closed domains in binary scale images are given.

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.

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.

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.

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.

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。

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。

算力网络研究进展:架构、关键技术与未来挑战

随着数字经济的蓬勃发展,算力已成为推动经济增长的核心动力。算力网络通过整合云、边、端的计算资源,实现资源的高效管理和调度,以支持多样化的数字应用需求。对算力网络进行全面综述,首先介绍了算力网络的概念和重要性,随后分析了其架构设计、算力资源度量和算力网络调度等关键技术。同时,探讨了算力网络在实现过程中遇到的挑战,包括技术融合、安全风险和资源优化等问题。最后,提出了算力网络未来的研究方向,旨在推动算力网络的进一步发展,为构建智能世界提供坚实的技术基础。

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。

多校区校园网一体化DNS网络架构研究

高校多校区一体化管理中,校区间人员流动频繁,如何保证学校师生在不同校区获得一致的上网体验成为一个难题。除了加快建设多活数据中心,实现重要信息系统的容灾备份体系之外,网络基础设施服务域名系统(Domain Name System,DNS)的一体化部署也面临新挑战。以河海大学南京校区和常州校区为例,通过多活容灾网络架构等技术手段部署一体化智能DNS服务,有效解决网络拥塞和信息系统访问瓶颈的问题,同时提升DNS管理的便捷性和服务的安全可靠性,对相关应用场景具有一定参考价值。

智能网联汽车多域电子电气架构技术发展研究

智能网联作为汽车行业的重要发展趋势,是实现汽车行业新四化技术改革的关键性支撑技术。创新升级智能网联汽车操作系统核心技术,不但是落实国家战略要求,实现汽车制造强国目的,更是掌握自主研发核心技术,打破发达国家对我国技术封锁的重要途径。现阶段而言,随着汽车的智能化程度越高、功能越丰富,其对操作系统的要求也越来越高,多域电子电器架构技术的重要性愈发凸显。基于此,文章在智能网联汽车多域电子电器架构技术的基础上,对多域电子电器架构技术发展现状进行了分析,指出了当前我国智能网联汽车电子电器架构技术的优势,并对智能网联汽车电子电器架构技术发展趋势进行了探讨。

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。

智能化空天防御作战体系架构设计方法研究

在潜在威胁日趋智能化和当前智能技术不断进步的双重推动下,空天防御作战体系不是装备和技术的线性叠加与机械堆砌,而是一个各作战要素有机融合、动态适变与持续演进的综合智能体。在分析智能化空天防御作战体系特征的基础上,创新智能化设计范式与理念,提出一种基于智能体网络的体系架构设计方法,从兵力节点、兵力关系、适变机制3个方面探讨了智能化赋能架构设计的关键技术。该方法为空天防御力量的顶层设计提供了智能化方法参考,有助于提升空天防御作战体系建设与运用的智能化、网络化水平。

应用人工神经网络预测室内全年动态采光

在建筑设计早期阶段,了解建筑形态参数与室内采光之间的关系对设计优化至关重要。本文采用多层感知器(Multilayer Perceptron,MLP)神经网络,以四种主要特征(室外遮挡情况、建筑形态特征、开窗设置、测点位置信息)作为MLP的输入参数,通过计算机模拟收集的数据来构建神经网络,预测室内的全年自然采光质量(UDI<100 lx、UDI 100~2000 lx、UDI>2000 lx)。研究结果显示多层感知器神经网络模型在测试集中的回归决定系数R 2为0.984,均方误差MSE为11.624,准确性较高。对神经网络进行权重分析的结果表明,外部遮挡物的高度和建筑进深对输出结果影响最为显著。而窗台底部的标高和测点距窗户的距离对输出结果UDI的影响较小。神经网络模型为建筑设计预测日光提供了一种新的智能方法,有助于辅助建筑早期的设计决策。

基于Swin-TCN融合网络的运动视频理解的研究

针对运动比赛中运动员行为识别的问题,提出一种基于Swin Transformer和TCN(时域卷积网络)的混合网络模型。首先对视频进行预处理得到视频帧,使用Swin模块提取视频帧中人体动作相关的空间特征,将输出的特征图的空间尺寸压缩,纵向拼接后交给TCN模块提取视频中人体动作的时序特征。为提高通道对于行为识别结果的贡献度,在TCN残差块中加入通道注意力机制,经过分类模块后得到最终结果。实验结果显示,该模型在UCF101上动作识别准确率达到了89.7%。