【标题】Toward artificially intelligent cloud-based building information modelling for collaborative multidisciplinary design
【参考中译】面向多学科协同设计的人工智能云化建筑信息建模
【类型】 期刊
【关键词】 Building information modelling; Concurrent engineering; Design collaboration; Knowledge graphs; Semantic enrichment
【参考中译】 建筑信息建模；并行工程；设计协作；知识图；语义丰富
【作者】 Rafael Sacks; Zijian Wang; Boyuan Ouyang; Duygu Utkucu; Siyu Chen
【摘要】 The technological tools people use for designing buildings have progressed from drawings to descriptive geometry, and from computer-aided drafting and design (CAD) to building information modelling (BIM). Yet despite their use of state-of-the-art BIM technology, the multidisciplinary teams that design modern buildings still face numerous challenges. Building models lack sufficient semantic content to properly express design intent, concurrent design is difficult due to the need for operators to maintain model consistency and integrity manually, managing design variations is cumbersome due to the packaging of information in files, and collaboration requires making-do with imperfect interoperability between application software. In response, we propose a 'Cloud BIM' (CBIM) approach to building modelling that seeks to automate maintenance of consistency across federated discipline-specific models by enriching models with semantic information that encapsulates design intent. The approach requires a new ontology to represent knowledge about the relationships between building model objects within and across disciplines. Discipline-specific building models are stored together with their data schema in knowledge graphs, and linked using objects and relationships from the CBIM ontology. The links are established using artificially intelligent semantic enrichment methods that recognize patterns of location, geometry, topology and more. Software methods that operate along CBIM relationship chains can detect inconsistencies that arise across disciplines and act to inform users, propose meaningful corrections, and apply them if approved. Future CBIM systems may provide designers with the functionality for collaborative multidisciplinary design by maintaining model consistency and managing versioning at the object level.
【参考中译】 人们用来设计建筑的技术工具已经从图纸到画法几何，从计算机辅助绘图和设计(CAD)到建筑信息建模(BIM)。然而，尽管他们使用了最先进的BIM技术，但设计现代建筑的多学科团队仍然面临着许多挑战。构建模型缺乏足够的语义内容来正确表达设计意图，并发设计由于操作员需要手动维护模型的一致性和完整性而困难，管理设计变化由于将信息打包在文件中而繁琐，以及协作需要在应用软件之间凑合不完善的互操作性。作为回应，我们提出了一种“Cloud BIM”(CBIM)方法来构建建模，该方法寻求通过使用封装设计意图的语义信息来丰富模型，从而自动维护联合规程特定模型之间的一致性。这种方法需要一个新的本体来表示关于学科内部和跨学科的建筑模型对象之间关系的知识。特定于学科的建筑模型与它们的数据模式一起存储在知识图中，并使用CBIM本体中的对象和关系进行链接。这些链接是使用人工智能语义丰富方法建立的，这些方法识别位置、几何、拓扑等模式。沿着CBIM关系链运行的软件方法可以检测跨学科出现的不一致，并采取行动通知用户，提出有意义的更正，并在获得批准后应用这些更正。未来的CBIM系统可以通过维护模型一致性和在对象级别管理版本来为设计者提供协作多学科设计的功能。
【来源】 Advanced engineering informatics 2022, vol.53
【入库时间】 2023/3/29
【标题】Artificial intelligence - enabled soft sensor and internet of things for sustainable agriculture using ensemble deep learning architecture
【参考中译】使用集成深度学习架构实现可持续农业的人工智能软测量和物联网
【关键词】 Agriculture; Predictive maintenance; CPS; Soft sensors; Deep learning; Feature representation; Classification
【参考中译】 农业；预测性维护；CPS；软测量；深度学习；特征表示；分类
【作者】 Anupong Wongchai; Surendra Kumar Shukla; Mohammed Altaf Ahmed; Ulaganathan Sakthi; Mukta Jagdish; Ravi kumar
【摘要】 IoT (Internet of things) and Artificial Intelligence (AI), as well as other advanced computing technologies, have long been used in agriculture.AI-enabled sensors function as smart sensors and IoT has made various types of sensor-based equipment in the field of agriculture. This research proposes novel techniques in AI technique based soft sensor integrated with remote sensing model using deep learning architectures. The input has been pre-processed to recognize the missing value, data cleaning and noise removal from the image which is collected from the agricultural land. The feature representation has been carried out usingweight-optimized neural network with maximum likelihood (WONN_ML). After representing the features, classification process has been carried out using ensemble architecture of stacked auto-encoder and kernel-based convolution network (SAE_KCN). The experimental results have been done for various crops in terms of computational time of 56%, accuracy 98%, precision of 85.5%, recall of 89.9% and F-1 score of 86% by proposed technique.
【参考中译】 物联网(IoT)和人工智能(AI)等先进计算技术在农业中的应用由来已久。AI使能的传感器起到智能传感器的作用，物联网在农业领域制造了各种基于传感器的设备。本研究提出了基于人工智能技术的软测量与基于深度学习的遥感模型集成的新技术。对采集到的农用地图像进行了缺失值识别、数据清洗和去噪处理。特征表示采用加权最大似然神经网络(WONN_ML)。在描述特征的基础上，利用堆叠式自动编码器和基于核的卷积网络的集成结构(SAE_KCN)进行分类处理。对不同作物的实验结果表明，该方法的计算时间为56%，准确率为98%，准确率为85.5%，召回率为89.9%，F-1评分为86%。
【来源】 Computers and Electrical Engineering 2022, vol.102
【标题】Design of English teaching system using Artificial Intelligence
【参考中译】基于人工智能的英语教学系统设计
【关键词】 Artificial intelligence; Internet of things video; Interpolation method; Teaching; Video processing
【参考中译】 人工智能；物联网视频；插值法；教学；视频处理
【作者】 Yuling Zhang; Jinping Cao
【摘要】 The process of English teaching is significantly affected by various technological advancements like the Internet of Things Video (IoTV). We start with the analysis of the actual teaching process of English and the direction of student feature recognition. We explore the IoTV reform to use the interpolation method for the student image-based feature recognition algorithm. Accordingly, we discover an intelligent algorithm for student feature recognition to improve the effectiveness of the IoTV. The intelligence algorithm for student feature recognition is designed using the Artificial Intelligence (AI) method. The proposed model combines voice recognition and text-to-speech technology to create functional modules of an English teaching system that change the traditional teaching mode and adjust teaching strategies in real-time based on student status based on feature recognition. Finally, this study demonstrates the practical effect of AI on the IoTV system through student feature recognition and classroom instruction assessment. Experiment outcomes reveal the efficiency of the AI-based IoTV system.
【参考中译】 物联网视频(IoTV)等各种技术的进步对英语教学过程产生了重大影响。我们从分析英语教学的实际过程和学生特征识别的方向入手。我们探索了在IoTV改革中使用插值法进行基于学生图像的特征识别算法。因此，我们发现了一种智能的学生特征识别算法，以提高IoTV的有效性。利用人工智能方法设计了学生特征识别的智能算法。该模型结合语音识别和文语转换技术，构建了一个英语教学系统的功能模块，改变了传统的教学模式，并基于特征识别根据学生身份实时调整教学策略。最后，本研究通过学生特征识别和课堂教学评估展示了人工智能在IoTV系统中的实际效果。实验结果表明了基于AI的IoTV系统的有效性。